Image edge pixel optimization method, device and device

Abstract

The present invention provides an image edge pixel optimization method, device and device, including: acquiring all edge pixels of a target image; deleting all to-be-optimized pixels in the edge pixels; When the edge pixel is deleted, the number of 8-connected connections corresponding to any one of its corresponding adjacent points in the 8-neighborhood has not changed. , the 8-connected links represent the pixels connected to each other in the 8-neighborhood. The invention realizes the redundant judgment of edge points according to a single judgment condition, greatly improves optimization efficiency, realizes edge m-path optimization, avoids the problem of edge shortening, and lays a good foundation for further edge repair and shortest path search applications.

Description

technical field [0001] The present invention relates to the technical field of image processing, and in particular, to a method, device and device for optimizing image edge pixels. Background technique [0002] The edge in the image is one of the most essential features of the image. The principle of image edge detection is to detect all the points in the image with large changes in gray value, and these points are connected to form several lines, and these lines can be called images. the edge of. Common edge detection algorithms include Soble edge detection, Laplacian edge detection and Canny edge detection. The obtained edge image contains a lot of redundant information, which occupies a lot of storage resources and computing resources, which is not conducive to feature extraction. The purpose of graphics thinning processing is to search the skeleton of the image and remove the redundant pixels in the image, thereby reducing the amount of information in the image without...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides an image edge pixel point optimization method, device and equipment, which are used to solve the problem of excessive redundant information in the edge image obtained by edge detection in the prior art, which contains a large amount of redundant information and occupies a large amount of storage resources and computing resources. At the same time, due to the redundant information in the edge, m-connectivity (mixed connectivity) cannot be guaranteed. When searching for a path along the edge, there is ambiguity, it is impossible to find a unique path, and there may be a technical defect of an infinite loop.

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