WO2024255425A1 - Acquisition d'image - Google Patents

Acquisition d'image Download PDF

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Publication number
WO2024255425A1
WO2024255425A1 PCT/CN2024/087890 CN2024087890W WO2024255425A1 WO 2024255425 A1 WO2024255425 A1 WO 2024255425A1 CN 2024087890 W CN2024087890 W CN 2024087890W WO 2024255425 A1 WO2024255425 A1 WO 2024255425A1
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WIPO (PCT)
Prior art keywords
image
preset
meet
image quality
generation model
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Ceased
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PCT/CN2024/087890
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English (en)
Chinese (zh)
Inventor
李若愚
唐董琦
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Alipay Hangzhou Information Technology Co Ltd
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Alipay Hangzhou Information Technology Co Ltd
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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G06V10/141—Control of illumination
    • 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
    • G06V10/993—Evaluation of the quality of the acquired pattern
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40—Document-oriented image-based pattern recognition
    • G06V30/42—Document-oriented image-based pattern recognition based on the type of document
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/64—Computer-aided capture of images, e.g. transfer from script file into camera, check of taken image quality, advice or proposal for image composition or decision on when to take image

Definitions

  • This document relates to the field of image recognition technology, and in particular to an image acquisition method, device and electronic equipment.
  • the method usually adopted is to continue shooting until the image obtained meets the user's needs. Therefore, it is necessary to provide a more efficient image acquisition method that can reduce the number of repeated shootings.
  • one or more embodiments of the present specification provide an image acquisition method, comprising: acquiring an image taken of a target object; when the image does not meet a preset image quality condition, inputting the image into a pre-trained cause generation model, determining the position of the image that does not meet the preset image quality condition through the cause generation model, and generating image reshooting guidance information for the determined position, wherein the cause generation model is a model for detecting the cause of the image not meeting the preset image quality condition and providing corresponding improvement suggestions for the detection result; and re-taking the image of the target object according to the image reshooting guidance information.
  • an image acquisition device including: an image acquisition module, which acquires an image shot for a target object; an image reshooting guidance information generation module, which, when the image does not meet a preset image quality condition, inputs the image into a pre-trained cause generation model, determines the position of the image that does not meet the preset image quality condition through the cause generation model, and generates image reshooting guidance information for the determined position, wherein the cause generation model is a model for detecting the cause causing the image to not meet the preset image quality condition and providing corresponding improvement suggestions for the detection result; and a control module, which re-shoots the image of the target object according to the image reshooting guidance information.
  • one or more embodiments of the present specification provide an electronic device, comprising: a processor; and a memory arranged to store computer-executable instructions, wherein when the executable instructions are executed, the processor can: obtain an image taken of a target object; when the image does not meet a preset image quality condition, input the image into a pre-trained cause generation model, determine the position of the image that does not meet the preset image quality condition through the cause generation model, and generate image reshooting guidance information for the determined position, wherein the cause generation model is a model for detecting the cause of the image not meeting the preset image quality condition and providing corresponding improvement suggestions for the detection result; and re-take an image of the target object according to the image reshooting guidance information.
  • one or more embodiments of the present specification provide a storage medium for storing computer-executable instructions, which implement the following process when executed by a processor: obtaining an image taken of a target object; when the image does not meet a preset image quality condition, inputting the image into a pre-trained cause generation model, determining the position of the image that does not meet the preset image quality condition through the cause generation model, and generating image reshooting guidance information for the determined position, wherein the cause generation model is a model for detecting the cause of the image not meeting the preset image quality condition and providing corresponding improvement suggestions for the detection result; and re-taking the image of the target object according to the image reshooting guidance information.
  • FIG1 is a schematic flow chart of an image acquisition method according to an embodiment of the present specification.
  • FIG2 is a schematic flow chart of an image acquisition method according to another embodiment of the present specification.
  • FIG3 is a schematic flow chart of an image acquisition method according to another embodiment of the present specification.
  • FIG4 is a schematic diagram of an image acquisition method according to an embodiment of the present specification.
  • FIG5 is a schematic block diagram of an image acquisition device according to an embodiment of the present specification.
  • FIG. 6 is a schematic block diagram of an electronic device according to an embodiment of the present disclosure.
  • One or more embodiments of the present specification provide an image acquisition method, device, and electronic device to solve the current problems.
  • the embodiment of this specification provides an image acquisition method, and the execution subject of the method can be a terminal device, which can be a certain terminal device such as a mobile phone, a tablet computer, or a computer device such as a laptop or a desktop computer, or an IoT device (specifically, a smart watch, a vehicle-mounted device, etc.).
  • the method can specifically include the following steps.
  • step S102 an image captured of a target object is acquired.
  • the method in one or more embodiments of this specification can be applied to scenarios where certain quality requirements are placed on the captured images, such as: capturing scene images with a mobile phone or smart camera, capturing face images with a mobile phone or smart camera, capturing ID images, etc.
  • the target object in an embodiment of this specification can be an ID, a face, or a scene, etc. In actual applications, the target object can also include multiple, for example, the target object includes two, namely an ID and a face, etc.
  • the target object may include the ID and the face.
  • the ID and the face may be photographed respectively by the camera component of the terminal device to obtain the corresponding ID image and face image.
  • the ID and the face may be captured by the camera component of the terminal device to obtain an image containing both the ID and the face.
  • step S104 when the image does not meet the preset image quality conditions, the image is input into a pre-trained cause generation model, the cause generation model is used to determine the position in the image that does not meet the preset image quality conditions, and generate image reshooting guidance information for the determined position.
  • the cause generation model is a model for detecting the reasons why the image does not meet the preset image quality conditions, and providing corresponding improvement suggestions for the detection results.
  • the preset image quality conditions are determined according to the user's shooting requirements for the target object. For example, when shooting a document or a face in the eKYC process, the preset image quality conditions may include one or more of the following: the shot image reaches a preset pixel size, the shot image reaches a preset resolution, and the shot image reaches a set exposure parameter. Accordingly, images that do not meet the preset image quality conditions usually include: overexposure of highlights (i.e., the highlight area is too bright).
  • the images include images with an exposure higher than a preset exposure threshold value, images that are too dark (i.e., image brightness lower than a preset brightness threshold value and/or image contrast lower than a preset contrast threshold value, etc.), and images that are blurred (i.e., images with pixels lower than a preset pixel threshold value, etc.).
  • the image obtained through step S102 may or may not meet the preset image quality conditions.
  • There are many ways to determine whether the image meets the preset image quality conditions such as: pre-setting multiple image quality conditions, and determining whether each image quality condition is met by comparing one by one, so as to determine whether the image meets the preset drawing quality conditions.
  • a corresponding algorithm such as a classification algorithm or a random forest algorithm, etc.
  • a corresponding model can be constructed using the selected algorithm, and the model can be used to determine whether the captured image meets the preset image quality conditions.
  • a cause generation model is pre-trained.
  • the cause generation model determines the specific location where the quality problem occurs in the image taken last time, detects the cause of the quality problem, and finally generates image reshoot guidance information for the specific location, thereby providing targeted improvement suggestions for the next image shooting.
  • the input data of the cause generation model is an image that does not meet the preset image quality conditions, which may specifically include the image, the information contained in the image, etc.
  • the output result of the cause generation model is the image reshoot guidance information, which can be presented in text form, for example: "The highlight in the upper left corner of the certificate is overexposed, please adjust the highlight area in the upper left corner when shooting", "The overall image is blurred, please keep the phone still when shooting", etc., and the image reshoot guidance information can also be played in the form of voice broadcast.
  • the image reshooting guidance information may at least include: specific locations of quality issues on corresponding shooting objects and shooting suggestions, and may also include reasons for unqualified quality, etc.
  • the pre-trained cause generation model can adopt a training model from image to text, and the training model can be constructed based on a neural network, such as by a convolutional neural network, by a transformer, etc.
  • the loss function of the training cause generation model can adopt a standard loss function in text generation.
  • a method for determining locations in an image that do not meet preset image quality conditions may be based on a generation model that may use a method of overall comparison of the captured image with a standard image, or may use an image segmentation method, i.e., using a segmentation algorithm to determine which pixel locations in the captured image do not meet a preset pixel size.
  • step S106 the target object is re-imaged according to the image re-shooting guidance information.
  • the generated image reshooting guidance information can be used to guide the user to reshoot the image of the target object in a targeted manner, thereby completing the image acquisition operation with fewer shots, which is beneficial to improving image shooting efficiency.
  • the embodiment of the present specification provides an image acquisition method, firstly acquiring an image shot for a target object, then determining whether the image meets a preset image quality condition, and when the image does not meet the preset image quality condition, inputting the image into a pre-trained cause generation model, determining the position in the image that does not meet the preset image quality condition through the cause generation model, and generating image reshoot guidance information for the determined position, and finally reshooting the target object according to the generated image reshoot guidance information, thereby completing image acquisition.
  • the pre-trained cause generation model can detect the cause of the image not meeting the preset image quality condition in the last shooting process, obtain the specific cause of the quality problem, and locate the position in the last shot image that does not meet the preset image quality condition, obtain the specific position that causes the quality problem, and finally generate image reshoot guidance information for the determined position to feedback to the user, thereby providing targeted improvement suggestions for the next shooting, which can effectively reduce the number of repeated shootings, improve image shooting efficiency, and thus improve user experience.
  • step S1022 a plurality of image samples taken for different shooting objects, the reason why each image sample does not meet the preset image quality condition, and the position of each image sample that does not meet the preset image quality condition are obtained.
  • the plurality of image samples may include one or more of the following images: historical images of different photographed subjects, original images of each photographed subject that do not meet preset image quality conditions, and images simulated based on the original images of each photographed subject.
  • multiple image samples acquired for different shooting objects, the reason why each image sample does not meet the preset image quality conditions, and the position of each image sample that does not meet the preset image quality conditions can be directly input into the cause generation model as input data for model training.
  • step S1022 may be performed as the following steps A1 and A2.
  • Step A1 Acquire multiple image samples taken for different objects.
  • Step A2 respectively determining the reason why each image sample does not meet the preset image quality condition and the position of each image sample that does not meet the preset image quality condition.
  • multiple image samples taken for different shooting objects can be first obtained, and the multiple image samples can be used as input data for model training. Then, the cause generation model determines that each image sample does not meet the preset The reasons for the image quality conditions and the positions of each image sample that do not meet the preset image quality conditions are determined, and then the network layer for generating reason text set in the reason generation model is used to generate corresponding image reshooting guidance information based on the reasons why each image sample does not meet the preset image quality conditions and the positions of each image sample that do not meet the preset image quality conditions.
  • the reasons why the image sample does not meet the preset image quality conditions include: a first quality problem and a second quality problem, and the first quality problem is an image quality problem in which the image granularity is greater than or equal to a preset image granularity threshold (or called a large-granularity quality problem), and the second quality problem is an image quality problem in which the image granularity is less than a preset image granularity threshold (or called a fine-granularity quality problem).
  • the processing of obtaining the position in each image sample that does not meet the preset image quality conditions in the above step S1022 can be executed as the following steps B1 and B2.
  • Step B1 When the reason why the image sample does not meet the preset image quality condition is the first quality problem, the position of each image sample that does not meet the preset image quality condition is determined based on the target detection algorithm.
  • Step B2 When the reason why the image sample does not meet the preset image quality condition is the second quality problem, the position of each image sample that does not meet the preset image quality condition is identified based on a semantic segmentation algorithm.
  • the target detection algorithm is an algorithm for finding the target of interest from the image, and classifying and locating the found target of interest.
  • the target of interest can be roughly framed out by the target detection algorithm.
  • the embodiment of this specification does not limit the specific target detection algorithm.
  • the TwoStage detection algorithm or the OneStage detection algorithm based on deep learning can be used.
  • the semantic segmentation algorithm is an algorithm that classifies each pixel in the image, thereby dividing the image into multiple regions containing different categories of information.
  • the semantic segmentation algorithm can accurately depict the outline of the target, and can provide coordinate information and classification information in each pixel.
  • the semantic segmentation algorithm can not only predict the position and category of the target object in the image, but also depict the boundaries between different types of target objects.
  • the embodiment of this specification does not limit the specific semantic segmentation algorithm.
  • the FCN Full Convolutional Network
  • the CRF Conditional Random Field algorithm
  • the position of the image sample that does not meet the preset image quality conditions can be determined based on the result determined by the target detection algorithm; when the reason why the image sample does not meet the preset image quality conditions is a fine-grained quality problem, the semantic segmentation algorithm can be used to identify the pixel points at which positions in the image sample do not meet the requirements, thereby identifying the position of the image sample that does not meet the preset image quality conditions.
  • Different algorithms can be used to determine the position of each image sample that does not meet the preset image quality conditions according to the different reasons why the image sample does not meet the preset image quality conditions. The location of the quality conditions makes the positioning method for image quality problems more flexible, which can not only effectively save resources, but also efficiently and accurately obtain positioning results that meet the requirements.
  • step S1024 based on multiple images, the reasons why each image sample does not meet the preset image quality conditions, and the positions in each image sample that do not meet the preset image quality conditions, the cause generation model is trained using a preset first loss function to obtain a trained cause generation model.
  • the preset first loss function adopts a cross entropy loss function.
  • the preset first loss function is used for cause generation model training, which belongs to a supervised training method. It can not only improve the efficiency of model training, but also improve the accuracy of model training output results.
  • the image acquisition method when the image acquisition method in the embodiment of this specification is used in a blockchain system based on electronic identity authentication information, the image acquisition method may include the following steps S202 - S208 .
  • S202 Collect an image of a designated certificate of the target user and/or an image of the target user taken for the target user to access the blockchain system based on electronic identity authentication information, and use the image of the designated certificate and/or the image of the target user taken as the image taken of the target object.
  • the target user in order to access the blockchain system based on electronic identity authentication information, the target user needs to take a corresponding image (which may be an image of a designated document, an image of the target user, or an image of a designated document and an image of the target user), collect the above image, and use the above image as the image taken of the target object.
  • a corresponding image which may be an image of a designated document, an image of the target user, or an image of a designated document and an image of the target user
  • the image is input into a pre-trained cause generation model, the cause generation model is used to determine the position of the image that does not meet the preset image quality conditions, and generate image reshoot guidance information for the determined position.
  • the cause generation model is a model for detecting the reasons why the image does not meet the preset image quality conditions and providing corresponding improvement suggestions for the detection results.
  • the image information capacity in the blockchain system can be increased while ensuring the security and reliability of the image information.
  • the image acquisition method of the embodiments of this specification may include the following steps S302-S308.
  • Step S302 Acquire an image captured of the target object.
  • step S302 can refer to the relevant content of the above step S102, which will not be repeated here.
  • Step S304 input the image into a pre-trained quality model to obtain a quality assessment result of the image.
  • the quality assessment results of the image include: unqualified quality (i.e., the image does not meet the preset image quality conditions) and qualified quality (i.e., the image meets the preset image quality conditions).
  • the quality model is used to determine whether the image meets the preset image quality conditions based on the image classification method, and the quality model is obtained by model training through image samples of different photographed objects and a preset second loss function.
  • the input data of the quality model are image samples of different photographed objects, i.e., the images obtained in the above step S302, and the output results of the quality model are images with quality assessment results, including qualified quality images and unqualified quality images.
  • the quality model can be constructed using a binary classification algorithm, and the images taken for the target object can be divided into qualified quality images (or high-quality images) and unqualified quality images (or low-quality images) through the quality model.
  • the second loss function may adopt a classification model loss function, specifically a cross entropy loss function, or a BCE-loss (binary cross entropy loss function).
  • the user continues to perform subsequent processes based on the acquired image.
  • Step S306 If the quality assessment result indicates that the image does not meet the preset image quality conditions, the image is input into a pre-trained cause generation model, and the cause generation model is used to determine the position in the image that does not meet the preset image quality conditions, and generate image reshoot guidance information for the determined position.
  • the cause generation model is a model for detecting the reasons why the image does not meet the preset image quality conditions, and providing corresponding improvement suggestions for the detection results.
  • Step S308 re-shooting the image of the target object according to the image re-shooting guidance information.
  • FIG4 takes the eKYC process as an example and shows a schematic principle diagram of an image acquisition method according to an embodiment of this specification.
  • the process of an image acquisition method provided by one or more embodiments of this specification in actual application is as follows.
  • a pre-trained quality model based on image classification is used to perform a binary quality classification on the image taken by the user to determine whether the image taken by the user is a high-quality image or a low-quality image.
  • the causes of quality problems in low-quality images are subdivided.
  • the causes of quality problems may include: highlights, low light, blur, etc.
  • the specific locations of quality problems in low-quality images are located in the cause generation model.
  • the embodiment of the present specification provides an image acquisition method, firstly acquiring an image shot for a target object, then determining whether the image meets a preset image quality condition, and when the image does not meet the preset image quality condition, inputting the image into a pre-trained cause generation model, determining the position in the image that does not meet the preset image quality condition through the cause generation model, and generating image reshoot guidance information for the determined position, and finally reshooting the target object according to the generated image reshoot guidance information, thereby completing image acquisition.
  • the pre-trained cause generation model can detect the cause of the image not meeting the preset image quality condition in the last shooting process, obtain the specific cause of the quality problem, and locate the position in the last shot image that does not meet the preset image quality condition, obtain the specific position that causes the quality problem, and finally generate image reshoot guidance information for the determined position to feedback to the user, thereby providing targeted improvement suggestions for the next shooting, which can effectively reduce the number of repeated shootings, improve image shooting efficiency, and thus improve user experience.
  • one or more embodiments of this specification also provide an image acquisition device, as shown in FIG5 .
  • the image acquisition device includes: an image acquisition module 410 , an image reshooting guidance information generation module 420 and a control module 430 .
  • the image acquisition module 410 acquires an image taken of a target object.
  • the image reshooting guidance information generation module 420 when the image does not meet the preset image quality conditions, inputs the image into a pre-trained cause generation model, determines the position in the image that does not meet the preset image quality conditions through the cause generation model, and generates image reshooting guidance information for the determined position.
  • the cause generation model is a model for detecting the cause of the image not meeting the preset image quality conditions and providing corresponding improvement suggestions for the detection results.
  • the control module 430 re-takes an image of the target object according to the image re-shooting guidance information.
  • the image acquisition module 410 is also used to collect images of the target user's designated certificate and/or the target user taken by the target user to access the blockchain system based on electronic identity authentication information, and use the images of the designated certificate and/or the target user taken as the images taken of the target object; the image acquisition device may also include: a storage module, if the image meets the preset image quality conditions, the image and/or the information contained in the image is stored in the blockchain system.
  • the image acquisition device may also include: a quality assessment result acquisition module, which inputs the image into a pre-trained quality model to obtain a quality assessment result of the image; an image reshooting guidance information generation module 420, which is also used to input the image into a pre-trained cause generation model if the quality assessment result indicates that the image does not meet the preset image quality conditions, determine the position in the image that does not meet the preset image quality conditions through the cause generation model, and generate image reshooting guidance information for the determined position.
  • a quality assessment result acquisition module which inputs the image into a pre-trained quality model to obtain a quality assessment result of the image
  • an image reshooting guidance information generation module 420 which is also used to input the image into a pre-trained cause generation model if the quality assessment result indicates that the image does not meet the preset image quality conditions, determine the position in the image that does not meet the preset image quality conditions through the cause generation model, and generate image reshooting guidance information for the determined position.
  • the embodiment of the present specification provides an image acquisition device, which first acquires an image shot for a target object through an image acquisition module, and then, when the image does not meet the preset image quality conditions, the image retake guidance information generation module inputs the image into a pre-trained cause generation model, determines the position in the image that does not meet the preset image quality conditions through the cause generation model, and generates image retake guidance information for the determined position, and finally controls the image retake guidance information generation module to generate the image retake guidance information for the determined position.
  • the module re-takes the image of the target object according to the generated image retake guidance information, thereby completing the image acquisition.
  • the pre-trained cause generation model can detect the reasons why the image did not meet the preset image quality conditions during the last shooting process, obtain the specific reasons for the quality problem, and locate the position in the last shot that does not meet the preset image quality conditions, obtain the specific position that causes the quality problem, and finally generate image retake guidance information for the determined position and feedback it to the user, so as to provide targeted improvement suggestions for the next shooting, which can effectively reduce the number of repeated shooting, improve image shooting efficiency, and thus improve user experience.
  • the electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors 501 and a memory 502, and the memory 502 may store one or more storage applications or data.
  • the memory 502 may be a short-term storage or a persistent storage.
  • the application stored in the memory 502 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the electronic device.
  • the processor 501 may be configured to communicate with the memory 502 to execute a series of computer executable instructions in the memory 502 on the electronic device.
  • the electronic device may also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input and output interfaces 505, and one or more keyboards 506.
  • the electronic device includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer executable instructions in the electronic device, and is configured to be executed by one or more processors.
  • the one or more programs include the following computer executable instructions: obtaining an image taken of a target object; when the image does not meet a preset image quality condition, inputting the image into a pre-trained cause generation model, determining the position in the image that does not meet the preset image quality condition through the cause generation model, and generating image reshooting guidance information for the determined position, the cause generation model is a model for detecting the cause of the image not meeting the preset image quality condition, and providing corresponding improvement suggestions for the detection result; and re-taking the image of the target object according to the image reshooting guidance information.
  • One or more embodiments of the present specification also propose a storage medium for storing computer executable instructions, which implement the following process when executed by a processor: obtaining an image taken of a target object; when the image does not meet the preset image quality conditions, inputting the image into a pre-trained cause generation model;
  • the cause generation model is used to determine the position in the image that does not meet the preset image quality conditions, and generate image reshoot guidance information for the determined position.
  • the cause generation model is a model for detecting the reasons why the image does not meet the preset image quality conditions, and providing corresponding improvement suggestions for the detection results; according to the image reshoot guidance information, the target object is re-imaged.
  • a typical implementation device is a computer.
  • the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
  • one or more embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.
  • These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby performing a computer-implemented process on the computer or other programmable device.
  • the instructions executed on other programmable devices provide steps for implementing the functions specified in one or more flows of the flowcharts and/or one or more blocks of the block diagrams.
  • a computing device includes one or more processors (CPU), input/output interfaces, network interfaces, and memory.
  • processors CPU
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and/or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information.
  • Information can be computer readable instructions, data structures, program modules or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.
  • computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
  • program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types.
  • the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network.
  • program modules may be located in local and remote computer storage media, including storage devices.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Signal Processing (AREA)
  • Image Analysis (AREA)

Abstract

Un ou plusieurs modes de réalisation de la présente description divulguent un procédé et un appareil d'acquisition d'image, ainsi qu'un dispositif électronique. Le procédé comprend les étapes consistant à : acquérir une image capturée relative à un objet cible ; lorsque l'image ne satisfait pas une condition de qualité d'image prédéfinie, entrer l'image dans un modèle de génération de cause pré-entraîné puis, au moyen du modèle de génération de cause, déterminer dans l'image un emplacement qui ne satisfait pas la condition de qualité d'image prédéfinie et générer des informations de guide de recapture d'image relatives à l'emplacement déterminé, le modèle de génération de cause étant un modèle utilisé pour détecter une cause impliquant que l'image ne satisfait pas la condition de qualité d'image prédéfinie et pour fournir une suggestion d'amélioration correspondante relative à un résultat de détection ; et, sur la base des informations de guide de recapture d'image, recommencer une capture d'image sur l'objet cible.
PCT/CN2024/087890 2023-06-15 2024-04-16 Acquisition d'image Ceased WO2024255425A1 (fr)

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