Image thinning and characteristic classification method used for product defect detection and quality control

Abstract

The invention discloses an image thinning and characteristic classification method used for product defect detection and quality control. A pixel of an image is subjected to binaryzation to form a pixel value set area corresponding to the image; end point removal is carried out before an image is skeletonized to eliminate noise influence; an image thinning direction is judged, image skeletonization is carried out, an image is subjected to continuous iteration thinning successively from the upper, lower, left and right pole positions of the image to the inside, and strict image skeletonization is carried out, wherein the strict skeletonization is carried out is characterized in that continuous iteration thinning is carried out successively from the north, the south, the west and the east points of the image. When the image is not thinned or reaches thinning frequencies, redundant end points are removed, and the image is smoothly processed. A round area forms a point after being thinned, and a skeleton keeps original information; the method consumes time like a Davies two-step method; and after an END point is defined, end point interference can be eliminated, and an improved thinning principle is cooperated to guarantee judgment validity.

Description

technical field [0001] The invention relates to the field of image detection, in particular to defect detection and product quality control methods for products such as electronics, printing, and glass panels. Background technique [0002] With the development of large-scale industrial production, people have higher and higher requirements for product quality control. The traditional way of using human eyes to detect products is far from meeting the needs of modern industrial production. It is the trend of the times to carry out defect detection and quality control of products. Image thinning technology, that is, skeletonization, is to reduce the binary image to a series of lines with a single pixel, eliminate a large amount of redundant information in the image, and accurately retain the original image information. In the defect detection of array graphics such as plasma panel printing graphics, For defects such as disconnection and burrs, it can be detected efficiently an...

AI Technical Summary

Problems solved by technology

[0020] The technical problem to be solved by the present invention is the problem that the circle cannot be thinned into points when skeletonizing the image, that is, the problem of uneven thinning in all directions, and the time-consuming algorithm cannot exceed the existing method, so as to effectively control the problem caused by noise. The thinning image error makes the subsequent processing and application of the skeletonized image more convenient and fast. At the same time, by classifying the skeletonized feature points, defects can be effectively detected and described

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