WO2019053509A2 - Système de caméra vestimentaire augmentée par l'utilisateur avec traitement d'images variable - Google Patents

Système de caméra vestimentaire augmentée par l'utilisateur avec traitement d'images variable Download PDF

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Publication number
WO2019053509A2
WO2019053509A2 PCT/IB2018/001140 IB2018001140W WO2019053509A2 WO 2019053509 A2 WO2019053509 A2 WO 2019053509A2 IB 2018001140 W IB2018001140 W IB 2018001140W WO 2019053509 A2 WO2019053509 A2 WO 2019053509A2
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WO
WIPO (PCT)
Prior art keywords
image
user
processing
wearable apparatus
feedback information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2018/001140
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English (en)
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WO2019053509A3 (fr
Inventor
Yonatan Wexler
Amnon Shashua
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Orcam Technologies Ltd
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Orcam Technologies Ltd
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Filing date
Publication date
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Publication of WO2019053509A2 publication Critical patent/WO2019053509A2/fr
Publication of WO2019053509A3 publication Critical patent/WO2019053509A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/0304—Detection arrangements using opto-electronic means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/98—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • G06V10/987—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns with the intervention of an operator
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/20—Scenes; Scene-specific elements in augmented reality scenes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/30—Scenes; Scene-specific elements in albums, collections or shared content, e.g. social network photos or video
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/50—Constructional details
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures

Definitions

  • This disclosure generally relates to wearable devices and methods for capturing and processing images from an environment of a user, and using information derived from captured images.
  • Embodiments consistent with the present disclosure provide devices and methods for automatically capturing and processing images from an environment of a user, and systems and methods for processing information related to images captured from the environment of the user.
  • a wearable apparatus including a wearable image sensor may be configured to capture a plurality of images from an environment of a user of the wearable apparatus.
  • the wearable apparatus may include at least one processing device.
  • the at least one processing device may be programmed to analyze at least one image to identify a visual context; determine, based on at least the visual context, feedback information for a user; provide the feedback information to the user; receive an input from the user, wherein the input reflects a determination by the user that the feedback information was insufficient or incorrect; and transmit, based on the input from the user, information related to the at least one image to an external device for additional processing.
  • a computer-implemented method may include causing an action to be executed based on capturing a plurality of images from an environment of a user of a wearable apparatus.
  • the method may include analyzing at least one image to identify a visual context; determining, based on at least the visual context, feedback information for a user; providing the feedback information to the user; receiving an input from the user, wherein the input reflects a determination by the user that the feedback information was insufficient or incorrect; and transmitting, based on the input from the user, information related to the at least one image to an external device for additional processing.
  • a wearable apparatus including a wearable image sensor may be configured to capture a plurality of images from an environment of a user of the wearable apparatus.
  • the wearable apparatus may include at least one processing device.
  • the at least one processing device may be programmed to analyze at least one image to determine a classification of the at least one image, wherein the classification includes at least a context associated with the at least one image; select, based on the classification, an image processing mode from a plurality of alternative image processing modes, the plurality of alternative image processing modes including at least a local processing mode and a remote processing mode; when the local processing mode is selected, process the at least one image locally by the at least one processing device; and when the remote processing mode is selected, transmit at least a portion of the at least one image to an external device for image processing.
  • a computer-implemented method may include causing an action to be executed based on capturing a plurality of images from an environment of a user of a wearable apparatus.
  • the method may include analyzing at least one image to determine a classification of the at least one image, wherein the classification includes at least a context associated with the at least one image; selecting, based on the classification, an image processing mode from a plurality of alternative image processing modes, the plurality of alternative image processing modes including at least a local processing mode and a remote processing mode; when the local processing mode is selected, processing the at least one image locally by the at least one processing device; and when the remote processing mode is selected, transmitting at least a portion of the at least one image to an external device for image processing.
  • non-transitory computer-readable storage media may store program instructions, which are executed by at least one processor and perform any of the methods described herein.
  • FIG. 1A is a schematic illustration of an example of a user wearing a wearable apparatus according to a disclosed embodiment.
  • FIG. IB is a schematic illustration of an example of the user wearing a wearable apparatus according to a disclosed embodiment.
  • Fig. I C is a schematic illustration of an example of the user wearing a wearable apparatus according to a disclosed embodiment.
  • Fig. ID is a schematic illustration of an example of the user wearing a wearable apparatus according to a disclosed embodiment.
  • FIG. 2 is a schematic illustration of an example system consistent with the disclosed embodiments.
  • FIG. 3 A is a schematic illustration of an example of the wearable apparatus shown in Fig.
  • Fig. 3B is an exploded view of the example of the wearable apparatus shown in Fig. 3 A.
  • FIG. 4A is a schematic illustration of an example of the wearable apparatus shown in Fig. IB from a first viewpoint.
  • FIG. 4B is a schematic illustration of the example of the wearable apparatus shown in Fig. IB from a second viewpoint.
  • Fig. 5A is a block diagram illustrating an example of the components of a wearable apparatus according to a first embodiment.
  • Fig. 5B is a block diagram illustrating an example of the components of a wearable apparatus according to a second embodiment.
  • Fig. 5C is a block diagram illustrating an example of the components of a wearable apparatus according to a third embodiment.
  • Fig. 6 illustrates an exemplary embodiment of a memory containing software modules consistent with the present disclosure.
  • FIG. 7 is a schematic illustration of an embodiment of a wearable apparatus including an orientable image capture unit.
  • FIG. 8 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.
  • FIG. 9 is a schematic illustration of a user wearing a wearable apparatus consistent with an embodiment of the present disclosure.
  • Fig. 10 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.
  • Fig. 1 1 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.
  • Fig. 12 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.
  • Fig. 13 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.
  • Fig. 14 is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.
  • Fig. 15 is a schematic illustration of an embodiment of a wearable apparatus power unit including a power source.
  • FIG. 16 is a schematic illustration of an exemplary embodiment of a wearable apparatus including protective circuitry.
  • Fig. 17 illustrates an exemplary embodiment of a memory containing software modules consistent with the present disclosure.
  • FIG. 18A is a schematic illustration of an example of the user wearing a wearable apparatus capturing images according to a disclosed embodiment.
  • Fig. 18B is a schematic illustration of an external device according to a disclosed embodiment.
  • FIG. 18C is a schematic illustration of an example of the user wearing a wearable apparatus capturing images according to a disclosed embodiment.
  • Fig. 18D is a schematic illustration of an external device according to a disclosed embodiment.
  • Fig. 19A is a schematic illustration of a user interacting with the external device according to a disclosed embodiment.
  • Fig. 19B is a schematic illustration of the external device according to a disclosed embodiment.
  • Fig. 20 is a flowchart of an example of a method for determining that feedback information provided by a wearable device is insufficient or incorrect, consistent with disclosed embodiments.
  • Fig. 21 A is a block diagram illustrating an example of the components of a wearable apparatus according to a disclosed embodiment.
  • Fig. 21 B is a block diagram illustrating an example of the components of a wearable apparatus according to a disclosed embodiment.
  • Fig. 21C is a block diagram illustrating an example of the components of a wearable apparatus according to a disclosed embodiment.
  • Fig. 22 is a block diagram of an exemplary memory of a wearable apparatus storing software modules and at least one database.
  • Fig. 23A illustrates a situation of a user with a wearable device in a private contextual environment.
  • Figs. 23B-23D are example illustrations of image data captured by an image sensor associated with a wearable apparatus, consistent with disclosed embodiments.
  • Fig. 24 is a flowchart of an example of a method for selecting an image processing mode from a plurality of alternative image processing modes, consistent with disclosed embodiments.
  • Fig. 1A illustrates a user 100 wearing an apparatus 1 10 that is physically connected (or integral) to glasses 130, consistent with the disclosed embodiments.
  • Glasses 130 may be prescription glasses, magnifying glasses, non-prescription glasses, safety glasses, sunglasses, etc. Additionally, in some embodiments, glasses 130 may include parts of a frame and earpieces, nosepieces, etc., and one or no lenses. Thus, in some embodiments, glasses 130 may function primarily to support apparatus 1 10, and/or an augmented reality display device or other optical display device.
  • apparatus 1 10 may include an image sensor (not shown in Fig. 1 A) for capturing real-time image data of the field-of-view of user 100.
  • image data includes any form of data retrieved from optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums. The image data may include video clips and/or photographs.
  • apparatus 1 10 may communicate wirelessly or via a wire with a computing device 120.
  • computing device 120 may include, for example, a smartphone, or a tablet, or a dedicated processing unit, which may be portable (e.g., can be carried in a pocket of user 100).
  • computing device 120 may be provided as part of wearable apparatus 1 10 or glasses 130, whether integral thereto or mounted thereon.
  • computing device 120 may be included in an augmented reality display device or optical head mounted display provided integrally or mounted to glasses 130.
  • computing device 120 may be provided as part of another wearable or portable apparatus of user 100 including a wrist-strap, a multifunctional watch, a button, a clip-on, etc. And in other embodiments, computing device 120 may be provided as part of another system, such as an on-board automobile computing or navigation system.
  • computing device 120 may include a Personal Computer (PC), laptop, an Internet server, etc.
  • Fig. I B illustrates user 100 wearing apparatus 1 10 that is physically connected to a necklace 140, consistent with a disclosed embodiment.
  • apparatus 1 10 may be suitable for users that do not wear glasses some or all of the time.
  • user 100 can easily wear apparatus 110, and take it off.
  • Fig. 1C illustrates user 100 wearing apparatus 1 10 that is physically connected to a belt 150, consistent with a disclosed embodiment.
  • apparatus 1 10 may be designed as a belt buckle.
  • apparatus 1 10 may include a clip for attaching to various clothing articles, such as belt 150, or a vest, a pocket, a collar, a cap or hat or other portion of a clothing article.
  • Fig. ID illustrates user 100 wearing apparatus 1 10 that is physically connected to a wrist strap 160, consistent with a disclosed embodiment.
  • apparatus 1 10 may include the ability to identify a hand-related trigger based on the tracked eye movement of a user 100 indicating that user 100 is looking in the direction of the wrist strap 160.
  • Wrist strap 160 may also include an accelerometer, a gyroscope, or other sensor for determining movement or orientation of a user's 100 hand for identifying a hand-related trigger.
  • FIG. 2 is a schematic illustration of an exemplary system 200 including a wearable apparatus 1 10, worn by user 100, and an optional computing device 120 and/or a server 250 capable of communicating with apparatus 1 10 via a network 240, consistent with disclosed embodiments.
  • apparatus 1 10 may capture and analyze image data, identify a hand-related trigger present in the image data, and perform an action and/or provide feedback to a user 100, based at least in part on the identification of the hand-related trigger.
  • optional computing device 120 and/or server 250 may provide additional functionality to enhance interactions of user 100 with his or her environment, as described in greater detail below.
  • apparatus 1 10 may include an image sensor system 220 for capturing real-time image data of the field-of-view of user 100.
  • apparatus 1 10 may also include a processing unit 210 for controlling and performing the disclosed functionality of apparatus 1 10, such as to control the capture of image data, analyze the image data, and perform an action and/or output a feedback based on a hand-related trigger identified in the image data.
  • a hand-related trigger may include a gesture performed by user 100 involving a portion of a hand of user 100.
  • a hand-related trigger may include a wrist-related trigger.
  • apparatus 1 10 may include a feedback outputting unit 230 for producing an output of information to user 100.
  • apparatus 1 10 may include an image sensor 220 for capturing image data.
  • image sensor refers to a device capable of detecting and converting optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums into electrical signals.
  • the electrical signals may be used to form an image or a video stream (i.e. image data) based on the detected signal.
  • image data includes any form of data retrieved from optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums.
  • image sensors may include semiconductor charge-coupled devices (CCD), active pixel sensors in complementary metal-oxide-semiconductor (CMOS), or N-type metal-oxide-semiconductor (NMOS, Live MOS).
  • CCD semiconductor charge-coupled devices
  • CMOS complementary metal-oxide-semiconductor
  • NMOS N-type metal-oxide-semiconductor
  • image sensor 220 may be part of a camera included in apparatus 1 10.
  • Apparatus 1 10 may also include a processor 210 for controlling image sensor 220 to capture image data and for analyzing the image data according to the disclosed embodiments.
  • processor 210 may include a "processing device” for performing logic operations on one or more inputs of image data and other data according to stored or accessible software instructions providing desired functionality.
  • processor 210 may also control feedback outputting unit 230 to provide feedback to user 100 including information based on the analyzed image data and the stored software instructions.
  • a "processing device” may access memory where executable instructions are stored or, in some embodiments, a "processing device” itself may include executable instructions (e.g., stored in memory included in the processing device).
  • the information or feedback information provided to user 100 may include time information.
  • the time information may include any information related to a current time of day and, as described further below, may be presented in any sensory perceptive manner.
  • time information may include a current time of day in a preconfigured format (e.g., 2:30 pm or 14:30).
  • Time information may include the time in the user's current time zone (e.g., based on a determined location of user 100), as well as an indication of the time zone and/or a time of day in another desired location.
  • time information may include a number of hours or minutes relative to one or more predetermined times of day.
  • time information may include an indication that three hours and fifteen minutes remain until a particular hour (e.g., until 6:00 pm), or some other predetermined time.
  • Time information may also include a duration of time passed since the beginning of a particular activity, such as the start of a meeting or the start of a jog, or any other activity.
  • the activity may be determined based on analyzed image data.
  • time information may also include additional information related to a current time and one or more other routine, periodic, or scheduled events.
  • time information may include an indication of the number of minutes remaining until the next scheduled event, as may be determined from a calendar function or other information retrieved from computing device 120 or server 250, as discussed in further detail below.
  • Feedback outputting unit 230 may include one or more feedback systems for providing the output of information to user 100.
  • the audible or visual feedback may be provided via any type of connected audible or visual system or both.
  • Feedback of information according to the disclosed embodiments may include audible feedback to user 100 (e.g., using a BluetoothTM or other wired or wirelessly connected speaker, or a bone conduction headphone).
  • Feedback outputting unit 230 of some embodiments may additionally or alternatively produce a visible output of information to user 100, for example, as part of an augmented reality display projected onto a lens of glasses 130 or provided via a separate heads up display in communication with apparatus 1 10, such as a display 260 provided as part of computing device 120, which may include an onboard automobile heads up display, an augmented reality device, a virtual reality device, a smartphone, PC, table, etc..
  • computing device refers to a device including a processing unit and having computing capabilities.
  • Some examples of computing device 120 include a PC, laptop, tablet, or other computing systems such as an on-board computing system of an automobile, for example, each configured to communicate directly with apparatus 1 10 or server 250 over network 240.
  • Another example of computing device 120 includes a smartphone having a display 260.
  • computing device 120 may be a computing system configured particularly for apparatus 1 10, and may be provided integral to apparatus 1 10 or tethered thereto.
  • Apparatus 1 10 can also connect to computing device 120 over network 240 via any known wireless standard (e.g., Wi-Fi, Bluetooth®, etc.), as well as near-filed capacitive coupling, and other short range wireless techniques, or via a wired connection.
  • computing device 120 is a smartphone
  • computing device 120 may have a dedicated application installed therein.
  • user 100 may view on display 260 data (e.g., images, video clips, extracted information, feedback information, etc.) that originate from or are triggered by apparatus 110.
  • user 100 may select part of the data for storage in server 250.
  • Network 240 may be a shared, public, or private network, may encompass a wide area or local area, and may be implemented through any suitable combination of wired and/or wireless communication networks.
  • Network 240 may further comprise an intranet or the Internet.
  • network 240 may include short range or near-field wireless communication systems for enabling communication between apparatus 110 and computing device 120 provided in close proximity to each other, such as on or near a user's person, for example.
  • Apparatus 1 10 may establish a connection to network 240 autonomously, for example, using a wireless module (e.g., Wi-Fi, cellular).
  • apparatus 1 10 may use the wireless module when being connected to an external power source, to prolong battery life.
  • apparatus 1 10 and server 250 may be accomplished through any suitable communication channels, such as, for example, a telephone network, an extranet, an intranet, the Internet, satellite communications, off-line communications, wireless communications, transponder communications, a local area network (LAN), a wide area network (WAN), and a virtual private network (VPN).
  • a telephone network such as, for example, a telephone network, an extranet, an intranet, the Internet, satellite communications, off-line communications, wireless communications, transponder communications, a local area network (LAN), a wide area network (WAN), and a virtual private network (VPN).
  • LAN local area network
  • WAN wide area network
  • VPN virtual private network
  • apparatus 1 10 may transfer or receive data to/from server 250 via network 240.
  • the data being received from server 250 and/or computing device 120 may include numerous different types of information based on the analyzed image data, including information related to a commercial product, or a person's identity, an identified landmark, and any other information capable of being stored in or accessed by server 250.
  • data may be received and transferred via computing device 120.
  • Server 250 and/or computing device 120 may retrieve information from different data sources (e.g., a user specific database or a user's social network account or other account, the Internet, and other managed or accessible databases) and provide information to apparatus 1 10 related to the analyzed image data and a recognized trigger according to the disclosed embodiments.
  • calendar-related information retrieved from the different data sources may be analyzed to provide certain time information or a time-based context for providing certain information based on the analyzed image data.
  • apparatus 1 10 may be associated with a structure (not shown in Fig. 3 A) that enables easy detaching and reattaching of apparatus 1 10 to glasses 130.
  • image sensor 220 acquires a set aiming direction without the need for directional calibration.
  • the set aiming direction of image sensor 220 may substantially coincide with the field-of- view of user 100.
  • a camera associated with image sensor 220 may be installed within apparatus 110 in a predetermined angle in a position facing slightly downwards (e.g., 5-15 degrees from the horizon). Accordingly, the set aiming direction of image sensor 220 may substantially match the field- of-view of user 100.
  • Fig. 3B is an exploded view of the components of the embodiment discussed regarding Fig. 3A.
  • Attaching apparatus 1 10 to glasses 130 may take place in the following way. Initially, a support 310 may be mounted on glasses 130 using a screw 320, in the side of support 310. Then, apparatus 110 may be clipped on support 310 such that it is aligned with the field-of-view of user 100.
  • the term "support” includes any device or structure that enables detaching and reattaching of a device including a camera to a pair of glasses or to another object (e.g., a helmet).
  • Support 310 may be made from plastic (e.g., polycarbonate), metal (e.g., aluminum), or a combination of plastic and metal (e.g., carbon fiber graphite). Support 310 may be mounted on any kind of glasses (e.g., eyeglasses, sunglasses, 3D glasses, safety glasses, etc.) using screws, bolts, snaps, or any fastening means used in the art.
  • plastic e.g., polycarbonate
  • metal e.g., aluminum
  • metal e.g., carbon fiber graphite
  • Support 310 may be mounted on any kind of glasses (e.g., eyeglasses, sunglasses, 3D glasses, safety glasses, etc.) using screws, bolts, snaps, or any fastening means used in the art.
  • support 310 may include a quick release mechanism for disengaging and reengaging apparatus 1 10.
  • support 310 and apparatus 1 10 may include magnetic elements.
  • support 310 may include a male latch member and apparatus 110 may include a female receptacle.
  • support 310 can be an integral part of a pair of glasses, or sold separately and installed by an optometrist.
  • support 310 may be configured for mounting on the arms of glasses 130 near the frame front, but before the hinge.
  • support 310 may be configured for mounting on the bridge of glasses 130.
  • apparatus 1 10 may be provided as part of a glasses frame 130, with or without lenses. Additionally, in some embodiments, apparatus 1 10 may be configured to provide an augmented reality display projected onto a lens of glasses 130 (if provided), or alternatively, may include a display for projecting time information, for example, according to the disclosed embodiments. Apparatus 1 10 may include the additional display or alternatively, may be in communication with a separately provided display system that may or may not be attached to glasses 130.
  • apparatus 1 10 may be implemented in a form other than wearable glasses, as described above with respect to Figs. I B - I D, for example.
  • Fig. 4A is a schematic illustration of an example of an additional embodiment of apparatus 1 10 from a first viewpoint. The viewpoint shown in Fig. 4A is from the front of apparatus 110.
  • Apparatus 110 includes an image sensor 220, a clip (not shown), a function button (not shown) and a hanging ring 410 for attaching apparatus 110 to, for example, necklace 140, as shown in Fig. IB.
  • the aiming direction of image sensor 220 may not fully coincide with the field-of-view of user 100, but the aiming direction would still correlate with the field-of-view of user 100.
  • FIG. 4B is a schematic illustration of the example of a second embodiment of apparatus
  • apparatus 1 10 may further include a clip 420.
  • User 100 can use clip 420 to attach apparatus 1 10 to a shirt or belt 150, as illustrated in Fig. 1C.
  • Clip 420 may provide an easy mechanism for disengaging and reengaging apparatus 1 10 from different articles of clothing.
  • apparatus 1 10 may include a female receptacle for connecting with a male latch of a car mount or universal stand.
  • apparatus 1 10 includes a function button 430 for enabling user 100 to provide input to apparatus 1 10.
  • Function button 430 may accept different types of tactile input (e.g., a tap, a click, a double-click, a long press, a right-to-left slide, a left-to-right slide).
  • each type of input may be associated with a different action. For example, a tap may be associated with the function of taking a picture, while a right-to-left slide may be associated with the function of recording a video.
  • apparatus 1 10 may be implemented in any suitable configuration for performing the disclosed methods.
  • the disclosed embodiments may implement an apparatus 1 10 according to any configuration including an image sensor 220 and a processor unit 210 to perform image analysis and for communicating with a feedback unit 230.
  • Fig. 5A is a block diagram illustrating the components of apparatus 1 10 according to an example embodiment.
  • apparatus 110 includes an image sensor 220, a memory 550, a processor 210, a feedback outputting unit 230, a wireless transceiver 530, and a mobile power source 520.
  • apparatus 1 10 may also include buttons, other sensors such as a microphone, and inertial measurements devices such as accelerometers, gyroscopes, magnetometers, temperature sensors, color sensors, light sensors, etc.
  • Apparatus 110 may further include a data port 570 and a power connection 510 with suitable interfaces for connecting with an external power source or an external device (not shown).
  • Processor 210 may include any suitable processing device.
  • processing device includes any physical device having an electric circuit that performs a logic operation on input or inputs.
  • processing device may include one or more integrated circuits, microchips, microcontrollers, microprocessors, all or part of a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), field-programmable gate array (FPGA), or other circuits suitable for executing instructions or performing logic operations.
  • the instructions executed by the processing device may, for example, be pre-loaded into a memory integrated with or embedded into the processing device or may be stored in a separate memory (e.g., memory 550).
  • Memory 550 may comprise a Random Access Memory (RAM), a Read-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium, a flash memory, other permanent, fixed, or volatile memory, or any other mechanism capable of storing instructions.
  • RAM Random Access Memory
  • ROM Read-Only Memory
  • hard disk an optical disk
  • magnetic medium a
  • apparatus 1 10 includes one processing device (e.g., processor 210), apparatus 1 10 may include more than one processing device.
  • Each processing device may have a similar construction, or the processing devices may be of differing constructions that are electrically connected or disconnected from each other.
  • the processing devices may be separate circuits or integrated in a single circuit.
  • the processing devices may be configured to operate independently or collaboratively.
  • the processing devices may be coupled electrically, magnetically, optically, acoustically, mechanically or by other means that permit them to interact.
  • processor 210 may process a plurality of images captured from the environment of user 100 to determine different parameters related to capturing subsequent images. For example, processor 210 can determine, based on information derived from captured image data, a value for at least one of the following: an image resolution, a compression ratio, a cropping parameter, frame rate, a focus point, an exposure time, an aperture size, and a light sensitivity. The determined value may be used in capturing at least one subsequent image. Additionally, processor 210 can detect images including at least one hand-related trigger in the environment of the user and perform an action and/or provide an output of information to a user via feedback outputting unit 230.
  • processor 210 can change the aiming direction of image sensor
  • processor 210 may recognize certain situations from the analyzed image data and adjust the aiming direction of image sensor 220 to capture relevant image data. For example, in one embodiment, processor 210 may detect an interaction with another individual and sense that the individual is not fully in view, because image sensor 220 is tilted down. Responsive thereto, processor 210 may adjust the aiming direction of image sensor 220 to capture image data of the individual. Other scenarios are also contemplated where processor 210 may recognize the need to adjust an aiming direction of image sensor 220.
  • processor 210 may communicate data to feedback-outputting unit 230, which may include any device configured to provide information to a user 100.
  • Feedback outputting unit 230 may be provided as part of apparatus 1 10 (as shown) or may be provided external to apparatus 1 10 and communicatively coupled thereto.
  • Feedback-outputting unit 230 may be configured to output visual or nonvisual feedback based on signals received from processor 210, such as when processor 210 recognizes a hand-related trigger in the analyzed image data.
  • feedback refers to any output or information provided in response to processing at least one image in an environment.
  • feedback may include an audible or visible indication of time information, detected text or numerals, the value of currency, a branded product, a person's identity, the identity of a landmark or other environmental situation or condition including the street names at an intersection or the color of a traffic light, etc., as well as other information associated with each of these.
  • feedback may include additional information regarding the amount of currency still needed to complete a transaction, information regarding the identified person, historical information or times and prices of admission etc. of a detected landmark, etc.
  • feedback may include an audible tone, a tactile response, and/or information previously recorded by user 100.
  • Feedback-outputting unit 230 may comprise appropriate components for outputting acoustical and tactile feedback.
  • feedback- outputting unit 230 may comprise audio headphones, a hearing aid type device, a speaker, a bone conduction headphone, interfaces that provide tactile cues, vibrotactile stimulators, etc.
  • processor 210 may communicate signals with an external feedback outputting unit 230 via a wireless transceiver 530, a wired connection, or some other communication interface.
  • feedback outputting unit 230 may also include any suitable display device for visually displaying information to user 100.
  • apparatus 1 10 includes memory 550.
  • Memory 550 may include one or more sets of instructions accessible to processor 210 to perform the disclosed methods, including instructions for recognizing a hand-related trigger in the image data.
  • memory 550 may store image data (e.g., images, videos) captured from the environment of user 100.
  • memory 550 may store information specific to user 100, such as image representations of known individuals, favorite products, personal items, and calendar or appointment information, etc.
  • processor 210 may determine, for example, which type of image data to store based on available storage space in memory 550.
  • processor 210 may extract information from the image data stored in memory 550.
  • apparatus 110 includes mobile power source 520.
  • mobile power source includes any device capable of providing electrical power, which can be easily carried by hand (e.g., mobile power source 520 may weigh less than a pound). The mobility of the power source enables user 100 to use apparatus 1 10 in a variety of situations.
  • mobile power source 520 may include one or more batteries (e.g., nickel-cadmium batteries, nickel-metal hydride batteries, and lithium-ion batteries) or any other type of electrical power supply.
  • mobile power source 520 may be rechargeable and contained within a casing that holds apparatus 1 10.
  • mobile power source 520 may include one or more energy harvesting devices for converting ambient energy into electrical energy (e.g., portable solar power units, human vibration units, etc.).
  • Mobile power source 520 may power one or more wireless transceivers (e.g., wireless transceiver 530 in Fig. 5A).
  • wireless transceiver refers to any device configured to exchange transmissions over an air interface by use of radio frequency, infrared frequency, magnetic field, or electric field.
  • Wireless transceiver 530 may use any known standard to transmit and/or receive data (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, or ZigBee).
  • wireless transceiver 530 may transmit data (e.g., raw image data, processed image data, extracted information) from apparatus 1 10 to computing device 120 and/or server 250.
  • Wireless transceiver 530 may also receive data from computing device 120 and/or server 250.
  • wireless transceiver 530 may transmit data and instructions to an external feedback outputting unit 230.
  • Fig. 5B is a block diagram illustrating the components of apparatus 110 according to another example embodiment.
  • apparatus 110 includes a first image sensor 220a, a second image sensor 220b, a memory 550, a first processor 210a, a second processor 210b, a feedback outputting unit 230, a wireless transceiver 530, a mobile power source 520, and a power connector 510.
  • each of the image sensors may provide images in a different image resolution, or face a different direction.
  • each image sensor may be associated with a different camera (e.g., a wide angle camera, a narrow angle camera, an IR camera, etc.).
  • apparatus 1 10 can select which image sensor to use based on various factors. For example, processor 210a may determine, based on available storage space in memory 550, to capture subsequent images in a certain resolution.
  • Apparatus 1 10 may operate in a first processing-mode and in a second processing-mode, such that the first processing-mode may consume less power than the second processing-mode.
  • apparatus 1 10 may capture images and process the captured images to make real-time decisions based on an identifying hand-related trigger, for example.
  • apparatus 1 10 may extract information from stored images in memory 550 and delete images from memory 550.
  • mobile power source 520 may provide more than fifteen hours of processing in the first processing-mode and about three hours of processing in the second processing-mode. Accordingly, different processing-modes may allow mobile power source 520 to produce sufficient power for powering apparatus 1 10 for various time periods (e.g., more than two hours, more than four hours, more than ten hours, etc.).
  • apparatus 1 10 may use first processor 210a in the first processing- mode when powered by mobile power source 520, and second processor 210b in the second processing- mode when powered by external power source 580 that is connectable via power connector 510.
  • apparatus 1 10 may determine, based on predefined conditions, which processors or which processing modes to use.
  • Apparatus 110 may operate in the second processing-mode even when apparatus 1 10 is not powered by external power source 580.
  • apparatus 1 10 may determine that it should operate in the second processing-mode when apparatus 1 10 is not powered by external power source 580, if the available storage space in memory 550 for storing new image data is lower than a predefined threshold.
  • apparatus 1 10 may include more than one wireless transceiver (e.g., two wireless transceivers). In an arrangement with more than one wireless transceiver, each of the wireless transceivers may use a different standard to transmit and/or receive data.
  • a first wireless transceiver may communicate with server 250 or computing device 120 using a cellular standard (e.g., LTE or GSM), and a second wireless transceiver may communicate with server 250 or computing device 120 using a short-range standard (e.g., Wi-Fi or Bluetooth®).
  • apparatus 1 10 may use the first wireless transceiver when the wearable apparatus is powered by a mobile power source included in the wearable apparatus, and use the second wireless transceiver when the wearable apparatus is powered by an external power source.
  • Fig. 5C is a block diagram illustrating the components of apparatus 1 10 according to another example embodiment including computing device 120.
  • apparatus 1 10 includes an image sensor 220, a memory 550a, a first processor 210, a feedback-outputting unit 230, a wireless transceiver 530a, a mobile power source 520, and a power connector 510.
  • computing device 120 includes a processor 540, a feedback-outputting unit 545, a memory 550b, a wireless transceiver 530b, and a display 260.
  • One example of computing device 120 is a smartphone or tablet having a dedicated application installed therein.
  • computing device 120 may include any configuration such as an on-board automobile computing system, a PC, a laptop, and any other system consistent with the disclosed embodiments.
  • user 100 may view feedback output in response to identification of a hand-related trigger on display 260. Additionally, user 100 may view other data (e.g., images, video clips, object information, schedule information, extracted information, etc.) on display 260. In addition, user 100 may communicate with server 250 via computing device 120.
  • processor 210 and processor 540 are configured to extract information from captured image data.
  • extract information includes any process by which information associated with objects, individuals, locations, events, etc., is identified in the captured image data by any means known to those of ordinary skill in the art.
  • apparatus 1 10 may use the extracted information to send feedback or other real-time indications to feedback outputting unit 230 or to computing device 120.
  • processor 210 may identify in the image data the individual standing in front of user 100, and send computing device 120 the name of the individual and the last time user 100 met the individual.
  • processor 210 may identify in the image data, one or more visible triggers, including a hand-related trigger, and determine whether the trigger is associated with a person other than the user of the wearable apparatus to selectively determine whether to perform an action associated with the trigger.
  • One such action may be to provide a feedback to user 100 via feedback-outputting unit 230 provided as part of (or in communication with) apparatus 1 10 or via a feedback unit 545 provided as part of computing device 120.
  • feedback-outputting unit 545 may be in communication with display 260 to cause the display 260 to visibly output information.
  • processor 210 may identify in the image data a hand-related trigger and send computing device 120 an indication of the trigger.
  • Processor 540 may then process the received trigger information and provide an output via feedback outputting unit 545 or display 260 based on the hand- related trigger. In other embodiments, processor 540 may determine a hand-related trigger and provide suitable feedback similar to the above, based on image data received from apparatus 110. In some embodiments, processor 540 may provide instructions or other information, such as environmental information to apparatus 110 based on an identified hand-related trigger. [0090] In some embodiments, processor 210 may identify other environmental information in the analyzed images, such as an individual standing in front user 100, and send computing device 120 information related to the analyzed information such as the name of the individual and the last time user 100 met the individual, in a different embodiment, processor 540 may extract statistical information from captured image data and forward the statistical information to server 250. For example, certain information regarding the types of items a user purchases, or the frequency a user patronizes a particular merchant, etc. may be determined by processor 540. Based on this information, server 250 may send computing device 120 coupons and discounts associated with the user's preferences.
  • apparatus 1 10 When apparatus 1 10 is connected or wirelessly connected to computing device 120, apparatus 1 10 may transmit at least part of the image data stored in memory 550a for storage in memory 550b. In some embodiments, after computing device 120 confirms that transferring the part of image data was successful, processor 540 may delete the part of the image data.
  • the term "delete" means that the image is marked as 'deleted' and other image data may be stored instead of it, but does not necessarily mean that the image data was physically removed from the memory.
  • apparatus 1 10 may include a camera, a processor, and a wireless transceiver for sending data to another device. Therefore, the foregoing configurations are examples and, regardless of the configurations discussed above, apparatus 1 10 can capture, store, and/or process images.
  • the stored and/or processed images or image data may comprise a representation of one or more images captured by image sensor 220.
  • a "representation" of an image (or image data) may include an entire image or a portion of an image.
  • a representation of an image (or image data) may have the same resolution or a lower resolution as the image (or image data), and/or a representation of an image (or image data) may be altered in some respect (e.g., be compressed, have a lower resolution, have one or more colors that are altered, etc.).
  • apparatus 1 10 may capture an image and store a representation of the image that is compressed as a .JPG file.
  • apparatus 1 10 may capture an image in color, but store a black-and-white representation of the color image.
  • apparatus 1 10 may capture an image and store a different representation of the image (e.g., a portion of the image).
  • apparatus 1 10 may store a portion of an image that includes a face of a person who appears in the image, but that does not substantially include the environment surrounding the person.
  • apparatus 1 10 may, for example, store a portion of an image that includes a product that appears in the image, but does not substantially include the environment surrounding the product.
  • apparatus 1 10 may store a representation of an image at a reduced resolution (i.e., at a resolution that is of a lower value than that of the captured image). Storing representations of images may allow apparatus 1 10 to save storage space in memory 550. Furthermore, processing representations of images may allow apparatus 1 10 to improve processing efficiency and/or help to preserve battery life.
  • any one of apparatus 1 10 or computing device 120 may further process the captured image data to provide additional functionality to recognize objects and/or gestures and/or other information in the captured image data.
  • actions may be taken based on the identified objects, gestures, or other information.
  • processor 210 or 540 may identify in the image data, one or more visible triggers, including a hand-related trigger, and determine whether the trigger is associated with a person other than the user to determine whether to perform an action associated with the trigger.
  • Some embodiments of the present disclosure may include an apparatus securable to an article of clothing of a user.
  • Such an apparatus may include two portions, connectable by a connector.
  • a capturing unit may be designed to be worn on the outside of a user's clothing, and may include an image sensor for capturing images of a user's environment.
  • the capturing unit may be connected to or connectable to a power unit, which may be configured to house a power source and a processing device.
  • the capturing unit may be a small device including a camera or other device for capturing images.
  • the capturing unit may be designed to be inconspicuous and unobtrusive, and may be configured to communicate with a power unit concealed by a user's clothing.
  • the power unit may include bulkier aspects of the system, such as transceiver antennas, at least one battery, a processing device, etc.
  • communication between the capturing unit and the power unit may be provided by a data cable included in the connector, while in other embodiments, communication may be wirelessly achieved between the capturing unit and the power unit.
  • Some embodiments may permit alteration of the orientation of an image sensor of the capture unit, for example to better capture images of interest.
  • Fig. 6 illustrates an exemplary embodiment of a memory containing software modules consistent with the present disclosure. Included in memory 550 are orientation identification module 601 , orientation adjustment module 602, and motion tracking module 603. Modules 601 , 602, 603 may contain software instructions for execution by at least one processing device, e.g., processor 210, included with a wearable apparatus. Orientation identification module 601 , orientation adjustment module 602, and motion tracking module 603 may cooperate to provide orientation adjustment for a capturing unit incorporated into wireless apparatus 110.
  • processing device e.g., processor 210
  • Fig. 7 illustrates an exemplary capturing unit 710 including an orientation adjustment unit 705.
  • Orientation adjustment unit 705 may be configured to permit the adjustment of image sensor 220.
  • orientation adjustment unit 705 may include an eye-ball type adjustment mechanism.
  • orientation adjustment unit 705 may include gimbals, adjustable stalks, pivotable mounts, and any other suitable unit for adjusting an orientation of image sensor 220.
  • Image sensor 220 may be configured to be movable with the head of user 100 in such a manner that an aiming direction of image sensor 220 substantially coincides with a field of view of user 100.
  • a camera associated with image sensor 220 may be installed within capturing unit 710 at a predetermined angle in a position facing slightly upwards or downwards, depending on an intended location of capturing unit 710. Accordingly, the set aiming direction of image sensor 220 may match the field-of-view of user 100.
  • processor 210 may change the orientation of image sensor 220 using image data provided from image sensor 220. For example, processor 210 may recognize that a user is reading a book and determine that the aiming direction of image sensor 220 is offset from the text. That is, because the words in the beginning of each line of text are not fully in view, processor 210 may determine that image sensor 220 is tilted in the wrong direction. Responsive thereto, processor 210 may adjust the aiming direction of image sensor 220.
  • Orientation identification module 601 may be configured to identify an orientation of an image sensor 220 of capturing unit 710.
  • An orientation of an image sensor 220 may be identified, for example, by analysis of images captured by image sensor 220 of capturing unit 710, by tilt or attitude sensing devices within capturing unit 710, and by measuring a relative direction of orientation adjustment unit 705 with respect to the remainder of capturing unit 710.
  • Orientation adjustment module 602 may be configured to adjust an orientation of image sensor 220 of capturing unit 710.
  • image sensor 220 may be mounted on an orientation adjustment unit 705 configured for movement.
  • Orientation adjustment unit 705 may be configured for rotational and/or lateral movement in response to commands from orientation adj stment module 602.
  • orientation adjustment unit 705 may be adjust an orientation of image sensor 220 via motors, electromagnets, permanent magnets, and/or any suitable combination thereof.
  • monitoring module 603 may be provided for continuous monitoring. Such continuous monitoring may include tracking a movement of at least a portion of an object included in one or more images captured by the image sensor. For example, in one embodiment, apparatus 110 may track an object as long as the object remains substantially within the field-of-view of image sensor 220. In additional embodiments, monitoring module 603 may engage orientation adjustment module 602 to instruct orientation adjustment unit 705 to continually orient image sensor 220 towards an object of interest. For example, in one embodiment, monitoring module 603 may cause image sensor 220 to adjust an orientation to ensure that a certain designated object, for example, the face of a particular person, remains within the field-of view of image sensor 220, even as that designated object moves about.
  • monitoring module 603 may continuously monitor an area of interest included in one or more images captured by the image sensor. For example, a user may be occupied by a certain task, for example, typing on a laptop, while image sensor 220 remains oriented in a particular direction and continuously monitors a portion of each image from a series of images to detect a trigger or other event. For example, image sensor 210 may be oriented towards a piece of laboratory equipment and monitoring module 603 may be configured to monitor a status light on the laboratory equipment for a change in status, while the user's attention is otherwise occupied.
  • capturing unit 710 may include a plurality of image sensors 220.
  • the plurality of image sensors 220 may each be configured to capture different image data.
  • the image sensors 220 may capture images having different resolutions, may capture wider or narrower fields of view, and may have different levels of magnification.
  • Image sensors 220 may be provided with varying lenses to permit these different configurations.
  • a plurality of image sensors 220 may include image sensors 220 having different orientations. Thus, each of the plurality of image sensors 220 may be pointed in a different direction to capture different images.
  • the fields of view of image sensors 220 may be overlapping in some embodiments.
  • the plurality of image sensors 220 may each be configured for orientation adjustment, for example, by being paired with an image adjustment unit 705.
  • monitoring module 603, or another module associated with memory 550 may be configured to individually adjust the orientations of the plurality of image sensors 220 as well as to turn each of the plurality of image sensors 220 on or off as may be required.
  • monitoring an object or person captured by an image sensor 220 may include tracking movement of the object across the fields of view of the plurality of image sensors 220.
  • Embodiments consistent with the present disclosure may include connectors configured to connect a capturing unit and a power unit of a wearable apparatus.
  • Capturing units consistent with the present disclosure may include least one image sensor configured to capture images of an environment of a user.
  • Power units consistent with the present disclosure may be configured to house a power source and/or at least one processing device.
  • Connectors consistent with the present disclosure may be configured to connect the capturing unit and the power unit, and may be configured to secure the apparatus to an article of clothing such that the capturing unit is positioned over an outer surface of the article of clothing and the power unit is positioned under an inner surface of the article of clothing.
  • FIG. 8 is a schematic illustration of an embodiment of wearable apparatus 110 securable to an article of clothing consistent with the present disclosure.
  • capturing unit 710 and power unit 720 may be connected by a connector 730 such that capturing unit 710 is positioned on one side of an article of clothing 750 and power unit 720 is positioned on the opposite side of the clothing 750.
  • capturing unit 710 may be positioned over an outer surface of the article of clothing 750 and power unit 720 may be located under an inner surface of the article of clothing 750.
  • the power unit 720 may be configured to be placed against the skin of a user.
  • Capturing unit 710 may include an image sensor 220 and an orientation adjustment unit
  • Power unit 720 may include mobile power source 520 and processor 210. Power unit 720 may further include any combination of elements previously discussed that may be a part of wearable apparatus 110, including, but not limited to, wireless transceiver 530, feedback outputting unit 230, memory 550, and data port 570.
  • Connector 730 may include a clip 715 or other mechanical connection designed to clip or attach capturing unit 710 and power unit 720 to an article of clothing 750 as illustrated in Fig. 8. As illustrated, clip 715 may connect to each of capturing unit 710 and power unit 720 at a perimeter thereof, and may wrap around an edge of the article of clothing 750 to affix the capturing unit 710 and power unit 720 in place. Connector 730 may further include a power cable 760 and a data cable 770. Power cable 760 may be capable of conveying power from mobile power source 520 to image sensor 220 of capturing unit 710. Power cable 760 may also be configured to provide power to any other elements of capturing unit 710, e.g., orientation adjustment unit 705.
  • Data cable 770 may be capable of conveying captured image data from image sensor 220 in capturing unit 710 to processor 800 in the power unit 720. Data cable 770 may be further capable of conveying additional data between capturing unit 710 and processor 800, e.g., control instructions for orientation adjustment unit 705.
  • FIG. 9 is a schematic illustration of a user 100 wearing a wearable apparatus 1 10 consistent with an embodiment of the present disclosure. As illustrated in Fig. 9, capturing unit 710 is located on an exterior surface of the clothing 750 of user 100. Capturing unit 710 is connected to power unit 720 (not seen in this illustration) via connector 730, which wraps around an edge of clothing 750.
  • connector 730 may include a flexible printed circuit board (PCB).
  • Fig. 10 illustrates an exemplary embodiment wherein connector 730 includes a flexible printed circuit board 765.
  • Flexible printed circuit board 765 may include data connections and power connections between capturing unit 710 and power unit 720.
  • flexible printed circuit board 765 may serve to replace power cable 760 and data cable 770.
  • flexible printed circuit board 765 may be included in addition to at least one of power cable 760 and data cable 770.
  • flexible printed circuit board 765 may be substituted for, or included in addition to, power cable 760 and data cable 770.
  • FIG. 1 1 is a schematic illustration of another embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure.
  • connector 730 may be centrally located with respect to capturing unit 710 and power unit 720. Central location of connector 730 may facilitate affixing apparatus 1 10 to clothing 750 through a hole in clothing 750 such as, for example, a button-hole in an existing article of clothing 750 or a specialty hole in an article of clothing 750 designed to accommodate wearable apparatus 1 10.
  • FIG. 12 is a schematic illustration of still another embodiment of wearable apparatus 1 10 securable to an article of clothing.
  • connector 730 may include a first magnet 731 and a second magnet 732.
  • First magnet 731 and second magnet 732 may secure capturing unit 710 to power unit 720 with the article of clothing positioned between first magnet 731 and second magnet 732.
  • power cable 760 and data cable 770 may also be included.
  • power cable 760 and data cable 770 may be of any length, and may provide a flexible power and data connection between capturing unit 710 and power unit 720.
  • first magnet 731 and second magnet 732 may further include a flexible PCB 765 connection in addition to or instead of power cable 760 and/or data cable 770.
  • first magnet 731 or second magnet 732 may be replaced by an object comprising a metal material.
  • Fig. 13 is a schematic illustration of yet another embodiment of a wearable apparatus 1 10 securable to an article of clothing.
  • Fig. 13 illustrates an embodiment wherein power and data may be wirelessly transferred between capturing unit 710 and power unit 720.
  • first magnet 731 and second magnet 732 may be provided as connector 730 to secure capturing unit 710 and power unit 720 to an article of clothing 750.
  • Power and/or data may be transferred between capturing unit 710 and power unit 720 via any suitable wireless technology, for example, magnetic and/or capacitive coupling, near field communication technologies, radiofrequency transfer, and any other wireless technology suitable for transferring data and/or power across short distances.
  • Fig. 14 illustrates still another embodiment of wearable apparatus 1 10 securable to an article of clothing 750 of a user.
  • connector 730 may include features designed for a contact fit.
  • capturing unit 710 may include a ring 733 with a hollow center having a diameter slightly larger than a disk-shaped protrusion 734 located on power unit 720.
  • disk-shaped protrusion 734 may fit tightly inside ring 733, securing capturing unit 710 to power unit 720.
  • Fig. 14 illustrates an embodiment that does not include any cabling or other physical connection between capturing unit 710 and power unit 720.
  • capturing unit 710 and power unit 720 may transfer power and data wirelessly. In alternative embodiments, capturing unit 710 and power unit 720 may transfer power and data via at least one of cable 760, data cable 770, and flexible printed circuit board 765.
  • Fig. 15 illustrates another aspect of power unit 720 consistent with embodiments described herein.
  • Power unit 720 may be configured to be positioned directly against the user's skin.
  • power unit 720 may further include at least one surface coated with a biocompatible material 740.
  • Biocompatible materials 740 may include materials that will not negatively react with the skin of the user when worn against the skin for extended periods of time. Such materials may include, for example, silicone, PTFE, kapton, polyimide, titanium, nitinol, platinum, and others.
  • power unit 720 may be sized such that an inner volume of the power unit is substantially filled by mobile power source 520.
  • the inner volume of power unit 720 may be such that the volume does not accommodate any additional components except for mobile power source 520.
  • mobile power source 520 may take advantage of its close proximity to the skin of user's skin. For example, mobile power source 520 may use the Peltier effect to produce power and/or charge the power source.
  • an apparatus securable to an article of clothing may further include protective circuitry associated with power source 520 housed in in power unit 720.
  • Fig. 16 illustrates an exemplary embodiment including protective circuitry 775. As illustrated in Fig. 16, protective circuitry 775 may be located remotely with respect to power unit 720. In alternative embodiments, protective circuitry 775 may also be located in capturing unit 710, on flexible printed circuit board 765, or in power unit 720.
  • Protective circuitry 775 may be configured to protect image sensor 220 and/or other elements of capturing unit 710 from potentially dangerous currents and/or voltages produced by mobile power source 520.
  • Protective circuitry 775 may include passive components such as capacitors, resistors, diodes, inductors, etc., to provide protection to elements of capturing unit 710.
  • protective circuitry 775 may also include active components, such as transistors, to provide protection to elements of capturing unit 710.
  • protective circuitry 775 may comprise one or more resistors serving as fuses.
  • Each fuse may comprise a wire or strip that melts (thereby braking a connection between circuitry of image capturing unit 710 and circuitry of power unit 720) when current flowing through the fuse exceeds a predetermined limit (e.g., 500 milliamps, 900 milliamps, 1 amp, 1.1 amps, 2 amp, 2.1 amps, 3 amps, etc.)
  • a predetermined limit e.g., 500 milliamps, 900 milliamps, 1 amp, 1.1 amps, 2 amp, 2.1 amps, 3 amps, etc.
  • the wearable apparatus may transmit data to a computing device (e.g., a smartphone, tablet, watch, computer, etc.) over one or more networks via any known wireless standard (e.g., cellular, Wi-Fi, Bluetooth®, etc.), or via near-filed capacitive coupling, other short range wireless techniques, or via a wired connection.
  • a computing device e.g., a smartphone, tablet, watch, computer, etc.
  • any known wireless standard e.g., cellular, Wi-Fi, Bluetooth®, etc.
  • near-filed capacitive coupling, other short range wireless techniques e.g., cellular, Wi-Fi, Bluetooth®, etc.
  • the data transmitted to the wearable apparatus and/or received by the wireless apparatus may include images, portions of images, identifiers related to information appearing in analyzed images or associated with analyzed audio, or any other data representing image and/or audio data.
  • an image may be analyzed and an identifier related to an activity occurring in the image may be transmitted to the computing device (e.g., the "paired device").
  • the wearable apparatus may process images and/or audio locally (on board the wearable apparatus) and/or remotely (via a computing device). Further, in the embodiments described herein, the wearable apparatus may transmit data related to the analysis of images and/or audio to a computing device for further analysis, display, and/or transmission to another device (e.g., a paired device).
  • a paired device may execute one or more applications (apps) to process, display, and/or analyze data (e.g., identifiers, text, images, audio, etc.) received from the wearable apparatus.
  • Some of the disclosed embodiments may involve systems, devices, methods, and software products for determining at least one keyword.
  • at least one keyword may be determined based on data collected by apparatus 1 10.
  • At least one search query may be determined based on the at least one keyword.
  • the at least one search query may be transmitted to a search engine.
  • At least one keyword may be determined based on at least one or more images captured by image sensor 220.
  • the at least one keyword may be selected from a keywords pool stored in memory.
  • OCR optical character recognition
  • at least one image captured by image sensor 220 may be analyzed to recognize: a person, an object, a location, a scene, and so forth.
  • the at least one keyword may be determined based on the recognized person, object, location, scene, etc.
  • the at least one keyword may comprise: a person's name, an object's name, a place's name, a date, a sport team's name, a movie's name, a book's name, and so forth.
  • At least one keyword may be determined based on the user's behavior.
  • the user's behavior may be determined based on an analysis of the one or more images captured by image sensor 220.
  • at least one keyword may be determined based on activities of a user and/or other person.
  • the one or more images captured by image sensor 220 may be analyzed to identify the activities of the user and/or the other person who appears in one or more images captured by image sensor 220.
  • at least one keyword may be determined based on at least one or more audio segments captured by apparatus 1 10.
  • at least one keyword may be determined based on at least GPS information associated with the user.
  • at least one keyword may be determined based on at least the current time and/or date.
  • At least one search query may be determined based on at least one keyword.
  • the at least one search query may comprise the at least one keyword.
  • the at least one search query may comprise the at least one keyword and additional keywords provided by the user.
  • the at least one search query may comprise the at least one keyword and one or more images, such as images captured by image sensor 220.
  • the at least one search query may comprise the at least one keyword and one or more audio segments, such as audio segments captured by apparatus 1 10.
  • the at least one search query may be transmitted to a search engine.
  • search results provided by the search engine in response to the at least one search query may be provided to the user.
  • the at least one search query may be used to access a database.
  • the keywords may include a name of a type of food, such as quinoa, or a brand name of a food product; and the search will output information related to desirable quantities of consumption, facts about the nutritional profile, and so forth.
  • the keywords may include a name of a restaurant, and the search will output information related to the restaurant, such as a menu, opening hours, reviews, and so forth.
  • the name of the restaurant may be obtained using OCR on an image of signage, using GPS information, and so forth.
  • the keywords may include a name of a person, and the search will provide information from a social network profile of the person.
  • the name of the person may be obtained using OCR on an image of a name tag attached to the person's shirt, using face recognition algorithms, and so forth.
  • the keywords may include a name of a book, and the search will output information related to the book, such as reviews, sales statistics, information regarding the author of the book, and so forth.
  • the keywords may include a name of a movie, and the search will output information related to the movie, such as reviews, box office statistics, information regarding the cast of the movie, show times, and so forth.
  • the keywords may include a name of a sport team
  • the search will output information related to the sport team, such as statistics, latest results, future schedule, information regarding the players of the sport team, and so forth.
  • the name of the sport team may be obtained using audio recognition algorithms.
  • wearable apparatus 1 10 may provide augmented feedback in response to a sufficiency or correctness indicator from a user. For example, wearable apparatus 1 10 may provide feedback following analysis of an image of an environment of the user. The user may then indicate whether the feedback is insufficient or is incorrect. In response to the indication, wearable apparatus 1 10 may undertake further processing or analysis to provide augmented feedback. Accordingly, by allowing for user interaction to determine a level of feedback, embodiments of the present disclosure may increase the accuracy and suitability of information provided to users over conventional systems, thereby providing an improvement.
  • embodiments of the present disclosure may increase the efficiency with which users may use wearable apparatuses as compared with conventional systems, thus improving over the conventional systems. Moreover, the initial feedback may be provided faster.
  • An insufficiency as indicated by the user and/or recognized by the system, may include non-identification of a person, text recognition having a low (e.g., below a threshold) associated certainty or confidence level, or the like.
  • An incorrectness as indicated by the user and/or recognized by the system, may include a wrong identification of a person, an incorrect text recognition, wrong identification of products, or the like.
  • the indication may be provided through the usage of a pairable or external device such as a cell or mobile phone, voice, text, or by using controls.
  • a wearable apparatus 1 10 may capture images (e.g., using a wearable image sensor) and/or sound (e.g., using a wearable audio sensor), and perform processing using first processing resources (e.g., resources of wearable apparatus 1 10 and/or remote resources, such as resources of a pairable device such as a smartphone, or a remote computing platform).
  • Wearable apparatus 1 10 may cause results of the processing to be displayed to the user, for example, on wearable apparatus 1 10 and/or on a paired device, such as a smartphone, a tablet, or the like.
  • a paired device such as a smartphone, a tablet, or the like.
  • wearable apparatus 1 10 may play audio communicating the results through a wearable speaker and/or may cause the paired device to play the audio through one or more speakers associated with the paired device. Additionally with or alternatively to visual and/or auditory communication, wearable apparatus 1 10 may provide tactile feedback through a haptic motor or other source of vibration.
  • wearable apparatus 1 10 may employ further (remote) processors and/or other tools, such as social networks, a stored address book associated with the user, a company directory associated with the user, or the like for identification.
  • wearable apparatus 110 may employ further processors if the user provides an indication that the identification provided in the communicated results is incorrect.
  • a user may provide a hint, e.g., indicating a relation between the user and the incorrectly identified user (e.g., university, work, etc.), indicating a frequency of interaction between the user and the incorrectly identified user (e.g., monthly, weekly, daily, etc.), or the like, such that the hint may, in turn, be used to conduct the further processing.
  • a hint e.g., indicating a relation between the user and the incorrectly identified user (e.g., university, work, etc.), indicating a frequency of interaction between the user and the incorrectly identified user (e.g., monthly, weekly, daily, etc.), or the like, such that the hint may, in turn, be used to conduct the further processing.
  • FIG. 17 illustrates an exemplary embodiment of a memory 1700 containing software modules consistent with the present disclosure.
  • a processing device such as processor 210, may be programmed to implement one or more modules.
  • memory 1700 may include an image analysis module 1702, a feedback module 1704, and an insufficiency module 17060. Modules
  • 1702, 1704, and 1706 may contain software instructions for execution by at least one processing device, e.g., processor 210, included with a wearable apparatus (e.g., wearable apparatus 1 10).
  • Image analysis module 1702, feedback module 1704, and insufficiency module 1706 may cooperate to analyze a captured image, obtain information based on the analysis, provide feedback to the user, and receive input from the user to provide results of additional processing.
  • Memory 1700 may comprise, at least in part, memory 550 described above, or may comprise a separate memory in communication with memory 550.
  • Memory 1700 and/or memory 550 may each comprise one or more volatile and/or non-volatile memories.
  • image analysis module 1702 may analyze at least one image captured by a wearable image sensor included in the wearable apparatus from an environment of a user of the wearable apparatus.
  • Image analysis module 1702 may include software instructions for receiving data from wearable apparatus 1 10, such as a wearable camera system, and may include software instructions for analyzing data obtained by wearable apparatus 1 10 to identify a person, an activity, an object, a situation, or the like.
  • Data received from a wearable apparatus may include audio and image data, captured by, for example, an audio sensor or an image sensor, respectively, associated with the wearable apparatus and/or related to the identified person, activity, object, or the like. Audio data captured by the microphone may be analyzed to identify audio associated with the identified person, activity, object, or the like.
  • Image data may include raw images and may include image data that has been processed.
  • Raw images may be provided, for example, in the form of still images and/or video data, either with or without embedded metadata.
  • image data and audio data may be preprocessed prior to processing by image analysis module 1702.
  • Preprocessing may include, for example, noise reduction, artifact removal, compression, and other image pre-processing techniques.
  • the processing itself may be performed using one or more image classifiers, one or more neural networks, or the like.
  • image analysis module 1702 may detect or identify a subset or portion of the captured data that includes at least one person, activity, object, or the like.
  • image analysis module 1702 may be configured to receive a plurality of images that include at least one person or object.
  • image analysis module 1702 may receive a plurality of images of an environment surrounding a user wearing the wearable device 1 10 and may identify which of the plurality of images include at least one person or object.
  • image analysis module 1702 may using one or more image classifiers one or more neural networks, or the like, as described above, to eliminate images in which the at least one person, activity, object, or the like does not appear.
  • Image analysis module 1702 may further analyze the at least one image to identify visual context associated with the one or more neural networks, or the like. For example, image analysis module 1702 may identify at least one of a hand gesture, a person, an object, or the like as the visual context. In embodiments where the processing and/or analysis is performed remotely from the wearable apparatus, image analysis module 1702 may receive, from the external device, a result of an analysis of the information related to the at least one image, and may provide feedback via feedback module 1704 to the user, based on at least the result, as explained below.
  • the external device may include at least one of a server, a smartphone, a tablet, or a smartwatch.
  • image analysis module 1702 may analyze the at least one image using a facial recognition algorithm designed to detect facial features (e.g. mouth, eyes, etc.), facial contours, paralinguistic indicators, such as facial gestures or expressions, body shape, or any other suitable identifying feature of a person. Additionally or alternatively, at least one person may be identified using a thermal signature algorithm designed to detect the presence of at least one person based on the heat generated by the at least one person. In such embodiments, the wearable device 1 10 may capture thermal images, either alone or in combination with visual images, for processing by the thermal signature algorithm. Thermal recognition of the at least one person may be desirable in implementations in which wearable apparatus 1 10 is operating in reduced lighting situations.
  • a facial recognition algorithm designed to detect facial features (e.g. mouth, eyes, etc.), facial contours, paralinguistic indicators, such as facial gestures or expressions, body shape, or any other suitable identifying feature of a person.
  • at least one person may be identified using a thermal signature algorithm designed to detect the presence of at least one person based on the
  • At least one person may be identified through application of one or more classification techniques.
  • at least one image classification technique may be used to classify at least one feature of an image, such as a particular activity of the user.
  • an image classification technique may include at least one or more of image enhancement, edge detection, convolution, image analysis, and data extraction. Specific examples of the methods for identifying at least one person or at least one object, are exemplary only, and a person of ordinary skill in the art will recognize other methods for identifying the at least one person or object that remain consistent with present disclosure.
  • a person may be detected using a face detection algorithm, using a neural network trained to detect faces and/or persons in images; a face recognition algorithm, using a neural network trained to identify people in images; an object detection algorithm, using a neural network trained to detect objects and/or associated characteristics in images; an object recognition algorithm, using a neural network trained to identify objects in images, or the like.
  • Feedback module 1704 may then obtain information based on a result of the analysis of the least one captured image and may provide the information to the user.
  • Providing the feedback information to the user may include transmitting the feedback information to the external device (e.g., a smartphone, a tablet, or the like).
  • Feedback may include a name of a person, a type of an object, an identification of a location, or the like.
  • feedback module 1704 may be further configured to determine or obtain information associated with the at least one person or object identified in the image(s).
  • Feedback module 1704 may, for example, access a local database (e.g., an address book associated with the user or the like) and/or one or more remote databases (e.g., a social network, a company directory, or the like available via server 250) to search for information based on the analysis.
  • a local database e.g., an address book associated with the user or the like
  • remote databases e.g., a social network, a company directory, or the like available via server 250
  • Information that may be obtained for a person identified in an image may further include a name, nickname, social security number, account number, or any other identifier of the at least one person.
  • Information obtained for an object identified in an image may further include a length, width, depth, GPS position of an object, brand of an object, a value or cost of an object, an occupancy of an object, or any other identifier or characteristic indicator of the at least one object.
  • feedback module 1704 may obtain or determine at least one activity of the person in relation to an object and search a local or remote database based on the activity or information related to the activity.
  • Feedback module 1704 may further analyze, in conjunction with image analysis module 1702, based on a first processing scheme, the at least one image. Feedback information provided to the user may be determined at least based on the analysis using the first processing scheme.
  • feedback module 1704 may determine feedback information based on a level of certainty or confidence. For example, analysis of the at least one image may include performing facial recognition, person identification, text recognition, or the like. These processes may output a result having an associated level of certainty or confidence. Feedback module 1704 may compare the level of certainty to a threshold and may determine that there is an insufficiency. Feedback module 1704 may determine that the detected value should be provided to the user as feedback information such that the user may provide an input that may be used to determine whether the feedback information including the detected value was correct. For example, the user may provide an indicator of correctness, as explained above.
  • Insufficiency module 1706 may receive one or more inputs from the user that includes an indication of insufficiency or incorrectness.
  • the user may provide the one or more inputs via wearable apparatus 1 10 and/or via an external device, such as a smartphone or tablet paired with wearable apparatus 1 10.
  • Information indicating that feedback information was insufficient or incorrect may include at least one of a non-identification of a person, a text recognition with low certainty, an incorrect identification of a person, an incorrect text recognition, or an incorrect identification of one or more products.
  • Insufficiency module 1706 may then transmit information (e.g., text, at least a portion of a captured image, sound, etc.) to an external device.
  • the external device may be configured to perform additional or alternative processing to determine additional feedback information for the user.
  • the external device may be configured to determine, based on information received from at least one social network, an identification of a person depicted in at least one image.
  • insufficiency module 1706 may perform additional or alternative processing itself.
  • Modules 1702, 1704, and 1706 may be implemented in software, hardware, firmware, a mix of any of those, or the like.
  • any one or more of modules 1702, 1704, and 1706 may, for example, be stored in processor 540 and/or located on server 250, which may include one or more processing devices.
  • Processing devices of server 250 may be configured to execute the instructions of modules 1702, 1704, and 1706.
  • modules 1702, 1704, and 1706 may be configured to interact with each other and/or other modules of server 250 and/or a wearable image sensor (such as a camera system) to perform functions consistent with disclosed embodiments.
  • any of the disclosed modules may each include dedicated sensors (e.g., IR, image sensors, etc.) and/or dedicated application processing devices to perform the functionality associated with each module.
  • FIGs. 18A-18D are schematic illustrations of an example of a user 1800 indicating incorrectness of received feedback.
  • user 1800 may wear a wearable apparatus having a capturing unit 710 consistent with an embodiment of the present disclosure (e.g., as shown in Fig. 9).
  • capturing unit 710 may be located on an exterior surface of the clothing 750 of user 1800.
  • Capturing unit 710 may also be connected to power unit 720 (not shown) via connector 730, which wraps around an edge of clothing 750.
  • capturing unit 710 may capture an image including person 1822, and a processor (e.g., included in the wearable apparatus and/or as part of an external device) may determine an identity of a person 1822 to provide feedback including an identification of the person depicted in the image.
  • a processor e.g., included in the wearable apparatus and/or as part of an external device
  • user 1800 may be in any location and engaging in any interaction encountered during the user's daily activities.
  • user 1800 may be at a convenience store, grocery store, sports event, social event, work-related event, movie theater, concert, or the like.
  • the wearable apparatus may capture a plurality of images depicting the environment to which the user 1800 is exposed and in which person 1822 is included.
  • the feedback including the identity may be displayed to user 1800, e.g., through smartphone 1801 , as depicted in Fig. 18B.
  • the feedback may additionally or alternatively include, e.g., a location in which person 1822 is located, an activity in which person 1822 is engaged, or other feedback based on visual context associated with person 1822.
  • the wearable apparatus may instead identify and/or determine visual context associated with an object, an activity, or the like.
  • a captured image may be analyzed to obtain information (that is, the feedback) as explained above with respect to image analysis module 1702.
  • the results of such analysis may then be displayed to user 1800, e.g., via smartphone 1801, as shown in Fig. 18B, and/or via the wearable apparatus or another external device paired with the wearable apparatus, such as a tablet.
  • a name (“Ted Truman") may be displayed on smartphone 1801 along with the captured image of person 1822.
  • the feedback information may not actually correspond to the person included in captured images.
  • user 1800 may provide input to smartphone 1801, the wearable apparatus, and/or another external device paired to the wearable apparatus that the feedback information (e.g., the name of "Ted Truman") is incorrect.
  • smartphone 1801 may display no name (and may instead inform user 1800 that no identity was determined) due, for example, to an inability to identify person 1822 using the processing described above. Accordingly, user 1800 may provide additional information to assist with identification of person 1822.
  • user 1800 may hold a piece of identifying information, such as business card 1842, or otherwise input additional information regarding person 1822 into smartphone 1801.
  • the additional information also referred to as a "hint"
  • the wearable apparatus may perform further analysis and/or transmit the additional information (in embodiments where input to the wearable apparatus) along with the captured image to an external device (e.g., a server, smartphone 1801, a tablet or the like) for additional processing.
  • an external device e.g., a server, smartphone 1801, a tablet or the like
  • the wearable apparatus may display the additional feedback to the user, e.g., using smartphone 1801 as shown in Fig. 18D.
  • a name (“Joe Taylor"
  • the name displayed in Fig. 18D is correct unlike the name displayed in Fig. 18B on account of the additional processing and, in the examples of Fig. 18A-18D, the additional information provided by user 1800 in the form of business card 1842.
  • the additional information may be input using a keyboard, a hand gesture, or the like.
  • additional feedback may be determined in response to input indicating incorrectness without additional information from user 1800.
  • Figs. 19A and 19B are an example of a user indicating insufficiency of provided feedback.
  • a wearable apparatus may have analyzed (and/or caused an external device to analyze) an image of person 1932 to determine feedback to be displayed to the user with smartphone 1946.
  • the user may interact with smartphone 1946 to indicate that the feedback information provided is insufficient (e.g., a name of person 1 32 or the like is missing).
  • smartphone 1946 in response to the indicator, may be configured to prompt the user to provide additional information for use in performing additional processing to determine additional feedback to supplement the provided feedback.
  • a user may be prompted to add or augment information regarding person 1932, after which the wearable apparatus and/or an external device (e.g., a server, smartphone 1946, a tablet, or the like) may undertake an additional round of processing using the image of person 1932 and the information provided by the user.
  • the external device may be configured to receive an input from the user related to the at least one image.
  • the input may include information related to a person, object, or location depicted in the at least one image.
  • the system may use the provided information in order to determine additional feedback information for the user.
  • the wearable apparatus may provide additional feedback to the user, e.g., as shown in Fig. 19B.
  • the additional feedback includes a location associated with person 1932 shown on smartphone 1946.
  • the wearable apparatus may instead identify and/or determine visual context associated with an object, an activity, or the like.
  • Fig. 20 is a flowchart of an example of a method 2000 for determining that feedback information provided by a wearable device is insufficient or incorrect, consistent with disclosed embodiments.
  • Method 2000 may be implemented by a general-purpose computer or a special-purpose computer built according to embodiments of the present disclosure.
  • method 2000 may be executed by at least one of processors 210, 210a, 210b, and 540 of wearable apparatus 1 10.
  • the steps of method 2000 may be performed by one or more external devices (e.g., a processor included in an external server that receives data from wearable apparatus 1 10 over a network and/or a processor included in a paired external device such as a laptop, smartwatch, smartphone, tablet, ear phones, etc.).
  • external devices e.g., a processor included in an external server that receives data from wearable apparatus 1 10 over a network and/or a processor included in a paired external device such as a laptop, smartwatch, smartphone, tablet, ear phones, etc.
  • the processor may analyze at least one image to identify a visual context.
  • a wearable image sensor included in a wearable apparatus may have previously captured the at least one image.
  • analysis module 1702 may include instructions for analyzing the at least one image to detect a person, an object, an activity, or the like.
  • analysis of a person included in the at least one image may be performed using a facial recognition algorithm designed to detect facial features (e.g. mouth, eyes, etc.), facial contours, paralinguistic indicators such as facial gestures or expressions, body shape, or any other suitable identifying feature of the person that identifies the visual context.
  • analysis of an object included in the at least one image may be performed using an object recognition algorithm designed to detect object features (e.g., boundaries, texture, color, or the like), object contours, or any other suitable identifying feature of the object that identifies the visual context.
  • object features e.g., boundaries, texture, color, or the like
  • object contours e.g., contours, or any other suitable identifying feature of the object that identifies the visual context.
  • the processor may determine, based on at least the visual context, feedback information for a user.
  • the feedback information may include, for example, a name, a type of an object, a location associated with the person, object, etc., or the like.
  • feedback information may be determined on the wearable apparatus and/or using an external device such as a server, a smartphone, a tablet, or the like.
  • the wearable apparatus and/or the external device may access one or more databases, such as an address book associated with the user, a social network, or the like, in order to determine the feedback information.
  • the processor may provide the feedback information to the user.
  • the processor may be configured to send an instruction to a display associated with an external device such that the feedback information is displayed on the display. Accordingly, feedback is thereby provided to the user.
  • a visual context such as a visual trigger, a hand gesture, an object, an event, an action, a person, textual information, etc.
  • the processor may use a first processing scheme to analyze the images and provide feedback to the user. Examples of a first processing scheme may include: local processing, remote processing, or the like.
  • the processor may receive an input from the user, the input reflecting a determination by the user that the feedback information was insufficient or incorrect.
  • the input may comprise a key press, a voice command, a hand gesture, or the like.
  • the determination by the user that the feedback information was insufficient or incorrect may include at least one of a non-identification of a person, a text recognition with low certainty, an incorrect identification of a person, an incorrect text recognition, or an incorrect identification of one or more products.
  • a user may provide a hint to assist the processor with further processor of the at least one image.
  • the user may provide input relating to a relationship between the user the person in the at least one image (e.g. university, work, etc.) or may provide other input, e.g., as shown in Fig. 18C.
  • the processor may determine that the provided feedback is insufficient and/or incorrect.
  • the processor may analyze images captured after the feedback was provided to determine that the feedback is insufficient (e.g., providing feedback related to an identified person but not an activity that the person is undertaking or the like) and/or incorrect (e.g., identifying the wrong person or the like).
  • the processor may transmit, based on the input from the user, information related to the at least one image to an external device for additional processing.
  • the wearable apparatus may switch to remote processing after determining and/or receiving input indicative that the provided feedback is insufficient and/or incorrect.
  • the processor may use a second processing scheme to analyze the images to provide additional feedback to the user.
  • Some examples of a possible second processing scheme may include: local processing, remote processing, or the like.
  • the first processing scheme may include local processing
  • the second processing scheme may include remote processing
  • the first processing mechanism may include remote processing
  • the second processing mechanism may include local processing
  • a wearable apparatus may select between processing modes, such as a local processing mode and a remote processing mode, based on one or more factors. For example, the detection of private data within captured images, audio, or the like might trigger processing solely within a wearable device or may permit access to particular external processing resources (e.g., having security deemed sufficient to protect the privacy of the data). Accordingly, one of either first processing or second processing may be performed based on a consideration of privacy.
  • the detection of a battery level below a particular threshold may trigger processing solely within a wearable device or on external processing resources within a closer range (e.g., a Bluetooth® range but not a Wi-Fi range). Accordingly, one of either first processing or second processing may be performed based on a consideration of battery level.
  • the selection of a processing mode may be made based on a type of processing associated with captured data. For example, captured images may be remotely processed while audio is locally processed. Accordingly, by allowing for factors such as content and battery level to determine a processing mode, embodiments of the present disclosure may increase efficiencies as compared with conventional systems, thereby providing an improvement. Moreover, by allowing for private data to be processed differently than non-private data, embodiments of the present disclosure may increase the privacy of users of wearable apparatuses as compared with conventional systems, thus improving over the conventional systems.
  • Fig. 21 A is a block diagram illustrating components of wearable apparatus 110 according to an example embodiment.
  • Fig. 21 A includes all the features of Fig. 5 A along with an audio sensor 21 10.
  • Audio sensor 21 10 may comprise a microphone or other sensor including a pressure sensor and encodes pressure differences comprising sound as a digital signal.
  • processor 210 may analyze signals from audio sensor 21 10 in addition to signals from image sensor 220.
  • FIG. 2 IB is a block diagram illustrating components of wearable apparatus 1 10 according to an example embodiment.
  • Fig. 21 B includes all the features of Fig. 5B along with audio sensor 21 10 Similar to Fig. 21 A, processor 210a may analyze signals from audio sensor 21 10 in addition to signals from image sensors 210a and 210b.
  • processor 210a may analyze signals from audio sensor 21 10 in addition to signals from image sensors 210a and 210b.
  • Figs. 21A and 21B each depict a single audio sensor, a plurality of audio sensors may be used, whether with a single image sensor as in Fig. 21 A or with a plurality of image sensors as in Fig. 2 IB.
  • Fig. 21 C is a block diagram illustrating components of wearable apparatus 1 10 according to an example embodiment.
  • Fig. 21 C includes all the features of Fig. 5C along with audio sensor 2110 Accordingly, wearable apparatus 1 10 may send data from audio sensor 21 10 to computing device 120 for analysis in addition to or in lieu of analyze the signals using processor 210.
  • wearable apparatus 1 10 may select between a first processing mode and a second processing mode.
  • the first processing mode may include a local processing mode for processing at least a portion of at least one captured image and/or at least a portion of captured audio by the at least one processing device (e.g., processor 210) of the wearable apparatus
  • the second processing mode may include a remote processing mode for processing at least a portion of the at least one captured image and/or at least a portion of captured audio by an external device (e.g., computing device 120).
  • the first processing mode may be triggered when the at least one image and/or the at least a portion of captured audio is classified as corresponding to a private context, and the second processing mode may be triggered when the classification corresponds to a non-private context.
  • the first processing mode may be triggered when the at least one image and/or the at least a portion of captured audio requires fewer processing resources (e.g., only one person to identify in the image, only one voice speaking in the audio, etc.), and the second processing mode may be triggered when the at least one image and/or the at least a portion of captured audio requires additional processing resources.
  • a determination of whether to use the first processing or the second processing may be based on a flag, and the flag may be set in response to certain criteria being met. For example, detection of certain indicators in captured images and/or audio may cause a flag value to be set to ON, as explained above.
  • wearable apparatus 110 may be configured to perform all of the classification of at least one image and/or at least some captured audio.
  • computing device 120 may be configured to perform at least some or all of the classification.
  • wearable apparatus 1 10 may transmit image and/or sound data, either unprocessed or preprocessed, to computing device 120 to assist wearable apparatus 110 with or perform in whole classification of the transmitted data to select the first processing mode or the second processing mode.
  • Fig. 22 illustrates an exemplary embodiment of a memory 2200 containing software modules consistent with the present disclosure.
  • a processing device such processor 210, may be programmed to implement one or more modules.
  • memory 2200 may include an image analysis module 2210, an audio analysis module 2220, a classification module 2230, and an action execution module 2240.
  • tasks performed by image analysis module 2210, audio analysis module 2220, classification module 2230, and action execution module 2240 may be allocated among wearable apparatus 1 10, computing device 120, and/or server 250.
  • Fig. 22 depicts a single memory unit, the modules depicted in Fig. 22 are not necessarily stored in a single memory unit.
  • memory 550a may store image analysis module 2210 and audio analysis module 2220
  • memory 550b may store classification module 2230 and action execution module 2240.
  • Other allocations are possible, and the present disclosure is not limited to the specific allocation described above.
  • memory 2200 may comprise, at least in part, memory 550 described above, may comprise a memory 550a and memory 550b, or may comprise an external memory (e.g., associated with server 205) in communication with memory 550, 550a, and/or 550b.
  • Memory 220 may comprise one or more volatile and/or non- volatile memories. Similar to modules 601, 602, and 603 shown in Fig. 6, the software modules shown in Fig. 22 may contain software instructions for execution by at least one processing device, e.g., processor 210 and/or processor 540.
  • Image analysis module 2210, audio analysis module 2220, classification module 2230, and action execution module 2240 may cooperate to perform one or more of image and/or audio classification and select between a first processing mode and a second processing mode based on the classification.
  • image analysis module 2210 may contain software instructions for analyzing one or more images and/or for performing optical character recognition (OCR) of at least one image captured by image sensor 220.
  • processor 210 may execute the image analysis module 2210 stored in memory 550a to perform analysis of one or more images captured by image sensor 220, and transmit a result of the analysis to computing device 120 via wireless transceiver 530a.
  • Processor 540 of computing device 120 may be programmed to receive the result via wireless transceiver 530b.
  • image analysis module 2210 upon execution by processor 210 and/or processor 540, may enable the processor to process captured image data and identify elements of images and/or textual information within the captured image data.
  • textual information consistent with the disclosed embodiments may include, but is not limited to, printed text (e.g., text disposed on a page of a newspaper, magazine, book), handwritten text, coded text, text displayed to a user through a display unit of a corresponding device (e.g., an electronic book, a television, a web page, or an screen of a mobile application), text disposed on a flat or curved surface of an object within a field-of- view of apparatus 1 10 (e.g., a billboard sign, a street sign, text displayed on product packaging), text projected onto a corresponding screen (e.g., during presentation of a movie at a theater), and any additional or alternate text disposed within images captured by image sensor 220.
  • printed text e.g., text disposed on a page of a newspaper,
  • image analysis module 2210 may provide functionality for apparatus 1 10 to analyze sets of real-time image data captured by image sensor 220.
  • Processor 210/540 may execute image analysis module 2210, for example, to determine the positions of objects in one or more sets of image data over time and determine the relative motion of those objects.
  • analyzing one or more images may involve edge identification, in which an image is analyzed to detect pixels at which discontinuities (e.g., sudden changes in image brightness) occur and edges (e.g., edges of the controllable object, a body part of the user, and/or an object associated with the user) are identified to coincide with the detected pixels.
  • analyzing one or more images may involve identifying in and/or extracting from an image pixels representative of one or more objects in the environment, such as an object, a body part of the user, and/or an object associated with the user.
  • Pixels may be determined to be representative of an object based on, for example, other images of the object maintained, e.g., in a database and/or predetermined data describing the object maintained, e.g., in a database (e.g., other images of the controllable device, of the body part of the user, and/or of the device associated with the user).
  • pixels may be determined to be representative of an object based on, for example, a trained neural network configured to detect predetermined objects (e.g., predetermined controllable devices, body parts of the user, and/or devices associated with the user).
  • Other types of analysis are possible as well, including, but not limited to, gradient matching, greyscale matching, scale-invariant feature transform (SIFT) matching, and/or interpretation trees.
  • SIFT scale-invariant feature transform
  • audio analysis module 2220 may contain software instructions for analyzing sound recorded by audio sensor 21 10.
  • audio sensor 21 10 may record sound continuously and store the recorded sound data in memory 2200 and/or transmit the recorded sound to an external device for storage (e.g., in memory 550b).
  • Memory 2200 may store the sound data in a buffer, which may have a size sufficient for storing a predetermined length of sound, such as 5 seconds, 10 seconds, 30 seconds, 60 seconds, etc.
  • processor 210 may transmit sound data to computing device 120, for example, for a designated time period (e.g., 5 seconds, 10 seconds, 30 seconds, 60 seconds, etc.).
  • processor 540 may execute software instructions of audio analysis module 2220, for example, to extract information from the sound data recorded before and/or after the recognition of the trigger.
  • Both image analysis module 2210 and audio analysis module 2220 may extract one or more features from captured images and captured audio, respectively.
  • the features may be extracted using one or more classifiers and/or one or more neural networks.
  • the features may comprise identification of persons, objects, locations, environments (e.g., a context such as home, work, confidential meeting, or the like) or the like in the images and/or any recognized text, as explained above.
  • the features may be fed to classification module 2230.
  • Classification module 2230 may determine a classification of the at least one image from image analysis module 2210 (and/or at least some audio from audio analysis module 2220) as having a first context or a second context. For example, classification module 2230 may select, based on the classification, an image processing mode from a plurality of alternative image processing modes, the plurality of alternative image processing modes including at least a local processing mode and a remote processing mode. Classification module 2230 may analyze image data captured by image sensor 220 and/or audio data captured by audio sensor 21 10 in response to detection of at least one parameter (e.g., at least one parameter determined based on at least one content classification of the image data and/or audio data) by image analysis module 2210 and/or audio analysis module 2220, respectively.
  • at least one parameter e.g., at least one parameter determined based on at least one content classification of the image data and/or audio data
  • the classification may correspond to a private or non-private context.
  • Classification module 2230 may be configured to further process the image data and/or the audio data, in accordance with a first processing or a second processing after determination of the classification.
  • Processing contexts classified by Classification module 2230 may be configured to identify, for example, a first processing including a local processing context for processing at least the portion of the at least one image by the at least one processing device of the wearable apparatus, and a second processing including a remote processing context for processing at least the portion of the at least one image by the external device.
  • classification module 2230 may be configured to determine classification based on at least the environment of the user or based on at least a person in the environment of the user. Additionally or alternatively, classification module 2230 may be configured to select at least one of the local and the remote processing modes based on at least one parameter, and the at least one parameter may be determined based on at least one content classification of the at least one image and/or at least some audio. In some examples, the processing may be based on at least one parameter, and the at least one parameter may be calculated based on the content of the captured images and/or audio.
  • the local processing mode may be triggered for processing visual content of the at least one image that involves a private context, such as a confidential meeting, a conversation at home rather than at work, or the like.
  • the remote processing mode may be triggered for processing visual content of the at least one image (or aural content of at least some captured audio) that involves accessing at least one external data source, such as a person that cannot be identified using data stored only on the wearable apparatus.
  • Each of the first, second, or more processing contexts may incorporate one or more of the local or remote modes discussed above, or other modes.
  • the at least one parameter may be set by the user, e.g., representing a predetermined decision by the user whether the at least one image and/or the at least some audio should be processed in accordance with a first, second, or more processing mode, e.g., a local processing mode, a remote processing mode, or the like.
  • a processing mode e.g., a local processing mode, a remote processing mode, or the like.
  • a user may input a threshold number of persons, objects, or the like, above which a different processing mode is selected.
  • a user may assign particular persons, objects, or the like to a difference processing mode than others.
  • Action execution module 2240 may provide functionality for apparatus 1 10 to execute various functions in response to stimuli, whether classifications (e.g. first or second processing contexts) detected by classification module 2230, appearance of objects or sounds within the vicinity of wearable apparatus 1 10, or other events detected as occurring while wearable apparatus 1 10 is in operation. Action execution module 2240 may, for example, coordinate the configuration and execution of one or more alternative actions that may be available to apparatus 1 10 upon positive identification of an object, a sound, or a particular situation.
  • classifications e.g. first or second processing contexts
  • These alternative actions may include, but not be limited to, suspending image capture by image sensor 220; suspending storage of images captured by image sensor 220; limiting information transmitted to a paired device (such as computing system 120); prohibiting all transmission of images captured by image sensor 220; enabling transmission of only images that have passed through classification module 2230; suspending capture of audio information from audio sensor 21 10; suspending transmission of audio information captured from audio sensor 21 10; prohibiting posting of images captured by image sensor 220 on social media; causing transmission to a paired mobile device (such as computing system 120) of information indicative of the identity of an individual detected in one or more images captured by image sensor 220, without transmitting those images to the paired mobile device, to thereby enable the paired device to execute a function relating to the individual without receiving the one or more images; displaying information relating to an individual detected in one or more images captured by image sensor 220; and blocking software development kit ("SDK”) functionality.
  • Action execution module 2240 may undertake any of these alternative actions in response to classification from classification module 2230 and/or
  • Local processing may determine feedback information for the user.
  • the remote processing mode is selected, at least a portion of the at least one image (and/or any recorded audio) may be transmitted to an external device (e.g., a server, a smartphone, a tablet, or the like) for processing, and the determined feedback may therefore be received from the external device.
  • the feedback information may include a name of a person, a type of an object, a location, or the like.
  • action execution module 2240 may install, e.g., on the wearable apparatus, one or more predetermined instructions configured to apply at least one of the local processing mode or the remote processing mode.
  • action execution module 2240 may be executed by a server or other external device in order to install the instructions on the wearable apparatus. Moreover, action execution module 2240 may change, based on one or more instructions received from the user, at least one of predetermined instructions. Alternatively, the wearable apparatus may change the one or more predetermined instructions in response to user input. For example, a user may change a threshold battery level at which local processing is activated, may change a set of persons, objects, activities, or the like for which remote processing is enabled, may change types of processing (e.g., person identification, object identification, or the like) that are permitted to use remote processing, or the like.
  • types of processing e.g., person identification, object identification, or the like
  • Image analysis module 2210, audio analysis module 2220, classification module 2230, and action execution module 2240 may be implemented in software, hardware, firmware, a mix of any of those, or the like.
  • image analysis module 2210, audio analysis module 2220, classification module 2230, and action execution module 2240 may include software, hardware, or firmware instructions (or a combination thereof) executable by one or more processors (e.g., processor 210 and/or processor 540), alone or in various combinations with each other.
  • the modules may be configured to interact with each other and/or other modules of apparatus 1 10 to perform functions consistent with disclosed embodiments.
  • the classification may be associated with one or more processing contextual situations.
  • the processing contextual situation may be a situation where the privacy of a person or a plurality of persons is of concern, a situation where a number of persons and/or objects above a threshold is included in one or more captured images, a situation where a battery level is below a threshold, a situation where processing of images uses data not stored on the wearable apparatus, a combination thereof, or the like.
  • the context may be determined using visual, audio, or textual clues.
  • visual clues associated with particular processing context may be predefined, and may include, but not be limited to, presence (whether visual or aural) of a person having an identity not stored on the wearable apparatus; presence (whether visual or aural) of an object having an identity not stored on the wearable apparatus; presence (whether visual or aural) of a number of persons above a threshold; visual indicators of privacy such as entry into a bathroom, exit from a bathroom, a child, nudity, a sign prohibiting recording, a tag associated with a limitation on recording, a predefined hand gesture, a restroom sign, a toilet, and/or a face of a particular individual; auditory indicators of privacy such as a verbal instruction prohibiting recording, a predefined phrase that is spoken or sound that is played, and/or a voice of a particular individual; or the like.
  • Figs. 23A-D show example environments having context that triggers a particular processing mode.
  • Figs. 23B-D are depicted as example fields of view that may be perceived by the user through a wearable apparatus equipped with an image sensor and/or an audio sensor.
  • the wearable apparatus may be associated with a pair of glasses 130 as discussed above.
  • the wearable apparatus may be attached to clothing worn by the user or may otherwise be associated with the user's body.
  • the wearable apparatus may be associated or affixed to a remote structure, including but not limited to a stick, a pole, a drone, a vehicle, a robot, or the like.
  • Fig. 23 A illustrates an exemplary scenario in which a wearable apparatus may detect a private context situation or a context situation with a number of persons and/or objects above a threshold.
  • user 2312 may enter a meeting room having more objects in the meeting room, such as conference table 2302, screen 2306, and clock 2304, which may be identifiable by capturing unit 710 of a wearable apparatus worn by user 2312.
  • the wearable apparatus may determine that the meeting room includes a private context situation in response to one or more stimuli.
  • screen 2306 may display information that is labeled as confidential, proprietary, or the like.
  • Documents 2318 may be labeled sensitive, confidential, privileged, or the like.
  • the conversation between persons 2314 and 2316 may include indicator words such as "confidential,” "privileged,” or the like.
  • the combination of a meeting room (indicating a work environment) along with the presence of persons 23 14 and/or 2316 (who may be a boss, such as a firm partner, or a co-worker on a sensitive project with user 2312) may together indicate a private situation.
  • the presence of two persons and/or the presence of at least three identifiable objects may indicate the need for higher processing and thus comprise a processing context.
  • the wearable apparatus may determine which of a plurality of processing types, including a local processing and a remote processing, to use to process information. If, for example information were to be sent to remote location for remote processing, there may be a risk that the information may be read by a third party, violating a privacy classification. Thus, local processing may be prioritized to prevent such exposure. On the other hand, if information were processed locally, there may be a risk that battery power would be consumed too quickly on account of the presence of a number of persons and/or objects above a threshold. Thus, remote processing may be prioritized to conserve battery power.
  • Fig. 23B depicts an example environment involving a person's private property.
  • the environment of Fig. 23B may be classified as private because the context includes a home rather than work or a public area. Additionally or alternatively, the environment of Fig. 23B may include a no trespassing sign 2310, further serving as a clue that the context is private.
  • the presence of the no trespassing sign 2310 may result in further actions in addition to classification of the context as private, such as disabling of all image capture and/or audio capture rather than merely selecting between local or remote processing.
  • the environment of Fig. 23 B may be classified as a home environment and thus an environment in which persons are likely to have locally stored profiles.
  • the wearable apparatus may determine which of a plurality of processing types, including a local processing and a remote processing, to use to process information. If, for example information were to be sent to remote location for remote processing, there may be a risk that the information may be read by a third party, violating a privacy classification. Thus, local processing may be prioritized to prevent such exposure. On the other hand, if information were to processed remotely, there may be inefficiencies in transferring images for identification of persons whose profiles are stored locally. Thus, local processing may be prioritized to provide feedback to the user more quickly.
  • Fig. 23C depicts an example environment involving a particular person 2320.
  • the identity of person 2320 may trigger a privacy context.
  • person 2320 may provide an audible warning to stop operation of the wearable apparatus, such as "TURN IT OFF! as shown in Fig. 23 C.
  • the wearable apparatus may determine whether person 2320 has a locally stored profile or not.
  • the wearable apparatus may determine which of a plurality of processing types, including a local processing and a remote processing, to use to process information. If, for example information were to be sent to remote location for remote processing, there may be a risk that the information may be read by a third party, violating a privacy classification. Thus, local processing may be prioritized to prevent such exposure. On the other hand, if information were to be processed locally, minimal information about person 2320 may be available due to the lack of a locally stored profile. Thus, remote processing may be prioritized to provide more accurate feedback to the user.
  • a local processing including a local processing and a remote processing
  • Fig. 23D depicts an example environment involving an environment with a large group of people, such as a movie theater.
  • the environment may include visual indicators of a private context, such as a movie screen and/or signs such as no cameras allowed sign 2330.
  • a movie preview may provide an audible warning to stop operation of the wearable apparatus, e.g., prior to the beginning of the movie.
  • the presence of the no cameras allowed sign 2330 may result in further actions in addition to classification of the context as private, such as disabling of all image capture and/or audio capture rather than merely selecting between local or remote processing.
  • the wearable apparatus may determine that a number of persons in the environment of Fig. 23D exceeds a threshold.
  • the presence of the number of persons exceeding the threshold may result in further actions in addition to classification of the context as private, such as disabling of person identification rather than merely selecting between local or remote processing.
  • the wearable apparatus may determine which of a plurality of processing types, including a local processing and a remote processing, to use to process information. If, for example information were to be sent to remote location for remote processing, there may be a risk that the information may be read by a third party, violating a privacy classification. Thus, local processing may be prioritized to prevent such exposure. On the other hand, if information were to processed locally, there may be inefficiencies on account of the large number of persons present in the picture, which may require large portions of processing resources and thus battery power. In addition, there may be inaccuracies due to the large number of persons, many of which may not have profiles that are stored locally. Thus, remote processing may be prioritized to provide more accurate feedback to the user and/or to conserve battery power.
  • a local processing including a local processing and a remote processing
  • Fig. 24 is a flowchart illustrating an example method 2400 for automatically varying processing modes associated with detected classifications of images and/or audio, consistent with disclosed embodiments.
  • Method 2400 may be implemented by a general-purpose computer or a special- purpose computer built according to embodiments of the present disclosure.
  • method 2400 may be executed by at least one of processors 210, 210a, 210b, and 540 of wearable apparatus 1 10.
  • the steps of method 2400 may be performed by one or more external devices (e.g., a processor included in an external server that receives data from wearable apparatus 1 10 over a network and/or a processor included in a paired external device such as a laptop, smartwatch, smartphone, tablet, ear phones, etc.).
  • external devices e.g., a processor included in an external server that receives data from wearable apparatus 1 10 over a network and/or a processor included in a paired external device such as a laptop, smartwatch, smartphone, tablet, ear phones, etc.
  • the processor may analyze at least one image to determine a classification of the at least one image.
  • the classification may include at least a context associated with the at least one image.
  • the processor may determine the classification of the at least one image as having a first processing context or a second processing context.
  • the processor may analyze at least some captured audio to determine a classification of the at least some audio as having a first processing context or a second processing context.
  • one or more classifiers and/or neural networks may be used to determine the classification.
  • processor 210 may select, based on the classification, an image processing mode from a plurality of alternative image processing modes.
  • the plurality of alternative image processing modes may include at least a local processing mode and a remote processing mode. At least one of the local and the remote processing modes may be based on at least one parameter, and the at least one parameter nay be determined based on at least one content classification of the at least one image. Additionally or alternatively, the first processing is triggered when the classification corresponds to a private context, and the second processing is triggered when the classification corresponds to a non-private context.
  • the processor may determine one or more further actions associated with the classification are executed by the processor in response to the classification.
  • the processor may process the at least one image locally by the at least one processing device. For example, the processor may process the at least one image to determine feedback information for the user.
  • the processor may transmit at least a portion of the at least one image to an external device for image processing. Accordingly, as explained above with respect to action execution module 2240, the feedback may be determined by the wearable apparatus or by the external device (e.g., a server, a smartphone, a tablet, or the like) depending on the classification.
  • the external device e.g., a server, a smartphone, a tablet, or the like
  • a user may modify the rules governing classification of an image and/or audio.
  • the feedback information may include a name of a person, a type of an object, a location, or the like.
  • any of the embodiments above may apply to audio processing rather than image processing or may apply to a combination thereof.
  • a first processing mode may be faster yet less accurate and/or detailed than a second processing mode.
  • a first processing mode may require greater processing resources than a second processing mode (e.g., include application of one or more classifiers, neural networks, or the like that are more processor-intensive than application of one or more classifiers, neural networks, or the like of the second processing mode).
  • a first processing mode may require greater memory resources than a second processing mode (e.g., include application of one or more classifiers, neural networks, or the like that are more memory-intensive than application of one or more classifiers, neural networks, or the like of the second processing mode).
  • Programs based on the written description and disclosed methods are within the skill of an experienced developer.
  • the various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software.
  • program sections or program modules can be designed in or by means of .Net Framework, .Net Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with included Java applets.

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Abstract

La présente invention concerne un système de caméra vestimentaire augmentée par l'utilisateur avec un traitement d'images variable basé sur un contenu. Dans un mode de réalisation, un appareil comprend un capteur d'images vestimentaire conçu pour capturer une pluralité d'images d'un environnement d'un utilisateur de l'appareil vestimentaire. L'appareil vestimentaire peut comprendre au moins un dispositif de traitement. Ledit dispositif de traitement peut être programmé pour analyser au moins une image pour identifier un contexte visuel ; déterminer, sur la base au moins du contexte visuel, des informations de retour pour un utilisateur ; fournir les informations de retour à l'utilisateur ; recevoir une entrée de l'utilisateur, l'entrée reflétant une détermination par l'utilisateur selon laquelle les informations de retour étaient insuffisantes ou incorrectes ; et transmettre à un dispositif externe, sur la base de l'entrée de l'utilisateur, des informations relatives à ladite image pour un traitement supplémentaire.
PCT/IB2018/001140 2017-09-18 2018-09-18 Système de caméra vestimentaire augmentée par l'utilisateur avec traitement d'images variable Ceased WO2019053509A2 (fr)

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US8180396B2 (en) * 2007-10-18 2012-05-15 Yahoo! Inc. User augmented reality for camera-enabled mobile devices
US20140253702A1 (en) * 2013-03-10 2014-09-11 OrCam Technologies, Ltd. Apparatus and method for executing system commands based on captured image data
US9070217B2 (en) * 2013-03-15 2015-06-30 Daqri, Llc Contextual local image recognition dataset
US9253266B2 (en) * 2013-05-03 2016-02-02 Spayce, Inc. Social interaction using facial recognition

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