JPH03665B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to an object recognition method for recognizing an object from a contour line in a television image.

Conventional object recognition methods of this type focus on the edges of each surface that makes up the object. Points with sudden changes in brightness in the image are extracted as edge points, and the edges are By connecting them, it is converted into a line drawing (line drawing).

For example, an example of the procedure for recognizing a circular object using the above method will be explained with reference to FIGS.
The original image taken with a television camera (Figure 1 a) is
First, differential processing is performed along the scanning line to extract one contour candidate point where the brightness suddenly changes (FIG. 1b). Next, perform similar differentiation processing on each pixel in the vicinity of this point, and consider the pixel with the largest differential value to be a point continuous to the contour candidate point described above.By repeating this operation, continuous contour points can be obtained. (contour candidate) is obtained (Fig. 1c), and when this contour point is closed (Fig. 1d), it is regarded as one object.

However, in such conventional contour line extraction methods, there are several factors that impede tracking of contour candidate points: (1) Blooming due to metallic luster (see Figure 2 a) (2) Overlapping objects (see Figure 2 b) (3) Unclear images due to rust, dirt, etc. on the surface of objects; (4) Image disturbances due to electrical noise; as a result, there are problems in which objects that should be present may be missed. Furthermore, in order to solve these problems, recognition algorithms become complex, making real-time processing almost impossible.

The present invention has been made in view of the above-mentioned circumstances, and an object of the present invention is to provide an object recognition method that can recognize the outline of a desired object at extremely high speed.

According to this invention, the outline of the object to be detected is known, and the positional relationships of several outline parts that can identify the outline are also known, and each pixel group is aligned with the outline of the object to be detected. The input image of a predetermined field of view in which the object to be detected is present is scanned using a filter means for specifying as one set at least three or more pixel groups having a positional relationship corresponding to The presence or absence of contour candidate points whose brightness changes suddenly within each group is detected, and when contour candidate points are detected simultaneously within each pixel group, it is recognized that the contour of the object to be detected exists. .

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

FIG. 3 is a schematic configuration diagram showing an example of an apparatus for carrying out the object recognition method according to the present invention, and shows a case where the ITV camera 2 is photographing the object 1 to be detected. ITV camera 2 detects the object 1
is photographed in a predetermined field of view, and a video composite signal containing the brightness and darkness signals of the input image is output to the synchronization separation circuit 3 and the A/D converter 4. The synchronization separation circuit 3 separates the synchronization signal from the input video composite signal, and uses the random access memory array (RAM array) based on this synchronization signal.
5 is specified, and the A/D converter 4 converts the brightness signal of the input video composite signal into image data with a brightness state of 16 gradations, and writes this into the specified address position. In this way
The RAM array 5 stores one screen worth of image data showing the brightness of the original image shown in FIG. Note that by specifying the X and Y addresses of the RAM array 5, arbitrary image data can be extracted.

On the other hand, the memory 6 stores a main program etc. for implementing the method of the present invention, and the central processing unit (CPU) 7 performs image processing of the image data in the RAM array 5 based on the main program contents. Execute.

Next, before explaining the image processing procedure of this CPU 7, the principle of the object recognition method according to the present invention will be explained.

The method of the present invention checks the presence or absence of a desired contour line based on whether there are contour features within three or more set ranges at the same time. That is, as shown in FIG. 5, there is a range A ₁ of ΔL ₁ in which the center position is separated by a distance L ₁ from the reference position P, and a range A 2 of ΔL ₂ in which the center position is separated by a distance L ₂ from _the reference position P. ,...A filter corresponding to the outline of the object to be detected is set to specify a range An of ΔLn whose center position is separated by a distance Ln from the reference position P as one set, and the reference position P is set as the scanning position. Scan the input screen and select each range A ₁ , A ₂ ,
...It is checked whether or not there are contour candidate points (filter feature points) whose brightness suddenly changes at the same time in An. For example, the detection target object 1 in this embodiment
As shown in Fig. 6, filters (hereinafter referred to as L-filters) that specify certain ranges A ₁ , A ₂ , and A ₃ of n=3 are set, and the filter position of this L-filter is (positions indicated by black circles), it is checked whether or not there are filter feature points in which contour candidate points exist at the same time within ranges A ₁ , A ₂ , and A ₃ . Note that the L-filter spacing L ₁ , L ₂ ,...
Ln is determined by the geometrical shape of the object to be detected, and is uniquely determined by the magnification rate of the television camera. Therefore, when determining the filter structure, complicated procedures such as learning are not necessary. Also, ΔL ₁ ,
ΔL ₂ , ...ΔLn are determined by noise, blur components, distortion of the optical system, etc.

Next, the processing procedure of the CPU 7 for executing the above-mentioned contents will be specifically explained in accordance with the flowchart shown in FIG. First, the filter position (scanning position) of the L-filter described above is moved to the starting point PST as shown in FIG. 4, and it is checked whether this scanning position is a filter feature point.

If the scanning position is not a filter feature point, the scanning position is moved to the right (X direction) by several pixels.
This amount of movement is determined by the check ranges ΔL ₁ , ΔL ₂ , and ΔL ₃ of the L-filter.

Then, it is determined whether the scanning position is at the rightmost end of the scanning range, and if it is not at the rightmost end, it is determined whether the current position is a filter feature point, and if it is not a filter feature point, the above process is performed. repeat.

In this way, when the scanning position reaches the rightmost end of the scanning range without any filter feature points, it is determined whether the scanning position is at the bottom end of the scanning range.

The scanning position is the lowest end of the scanning range (i.e. the fourth
In the case of position PED) in the figure, it is determined that there is no object to be detected on this screen, and image processing ends. On the other hand, if the scanning position is not at the bottom end of the scanning range, the scanning position is returned to the leftmost end of the scanning range and moved downward (in the Y direction) by a certain distance (ΔY) (see FIG. 4). Then, scanning is executed again from that scanning position to check the presence or absence of filter feature points.

The screen is scanned with the L-filter as described above, and when a filter feature point is detected, it is determined that "detection target object 1 is detected" at that scanning position.

In addition, in this L-filter, as shown in FIG. 6, the filter feature points appear when crossing the section l at the approximate center of the detection target object 1, so it is difficult to specify the exact position of the detection target object 1. Can not. Therefore, in this case, by moving the L-filter in the vertical direction (sub-scanning direction) to examine the range where the filter feature points exist, the accurate position of the detection target object 1 can be specified. Can be done.
Furthermore, by adding a determination that the detection target object 1 exists only when the tracking length of the filter feature point in the sub-scanning direction is equal to or greater than a certain reference length, object recognition with a higher accuracy rate can be achieved.

Also, the L-filter has a certain range A ₁ , A ₂ ...An
are arranged on the same scanning line.
As shown in FIGS. 11 to 11, several pixels in various directions and in individual ranges can be set to line up toward the filter position (position indicated by a black circle).

In addition, since the L-filter has directionality, for example, the L-filter can be set according to the object to be detected.
The screen is scanned by a filter, and if it is determined that there is no object to be detected, the L-filter is rotated by a predetermined angle and scanned again by the L-filter, which detects the object to be detected. It is also possible to repeatedly execute the process until it is determined that the detection target object is present, and to infer in which direction the detection target object exists based on the turning angle of the L-filter when the detection target object is detected.

As explained above, according to the present invention, the entire circumference of the contour is not traced, but pixel groups at several locations where the contour can be identified are extracted using an L-filter, and contour candidate points are simultaneously detected within each pixel group. Since the outline of the object to be detected is recognized as being present when it is detected, the outline recognition algorithm is simplified and the processing time can be shortened.
Furthermore, since the sensitivity to noise is low, the accuracy rate for finding the target object is high.

[Brief explanation of the drawing]

Figures 1 a to d are diagrams used to explain the procedure for object recognition using the conventional contour extraction method, and Figures 2 a and b are diagrams showing examples of factors that inhibit conventional contour tracing, respectively. , FIG. 3 is a schematic configuration diagram showing an example of an apparatus for implementing the object recognition method according to the present invention, FIG. 4 is a diagram showing the brightness of image data stored in the RAM array of FIG. 3, and FIG. 6 and 6 are diagrams respectively used to explain the L-filter according to the present invention, FIG. 7 is a flowchart showing an example of the processing procedure of the central processing unit, and FIG.
FIGS. 11 to 11 are diagrams showing other embodiments of the L-filter. 1... Detection target object, 2... ITV camera, 3
... Synchronization separation circuit, 4 ... A/D converter, 5 ...
RAM array, 6... memory, 7... central processing unit (CPU).

Claims (1)

[Claims] 1 At least three or more pixel groups in a positional relationship where each pixel group corresponds to the outline of the object to be detected are
Using filter means to specify as a set,
The input image of a predetermined field of view in which the object to be detected exists is scanned, and the presence or absence of contour candidate points whose brightness suddenly changes is detected within the set of pixel groups, and simultaneously within each pixel group of the set. An object recognition method that recognizes the existence of a contour line of an object to be detected when a contour candidate point is detected.