Workpiece image sparse stereo matching method based on improved-type shape context

Abstract

The invention provides a workpiece image sparse stereo matching method based on an improved-type shape context. The method integrates a shape context capable of reflecting a point position distribution relationship and gradient direction histogram features capable of reflecting point gradient attributes, and mainly comprises steps: pretreatment, such as gray normalization and Otsu binaryzation, is carried out on a left-right image pair comprising workpieces; Canny edge extraction is carried out on a binary image, and discrete edge points are obtained through uniform sampling; according to the histogram distribution of the shape context, a candidate matching point collection is determined, a similarity measurement and calculation formula is improved, and rough matching of the shape context is carried out; the gradient direction histogram features are used for fine matching of the gradient direction histograms; and left-right consistency is introduced for calibrating and removing error matching point pairs. In the condition of meeting real-time performance requirements, the original shape context matching precision and the matching robustness can improved, and a foundation is laid for realizing quick and accurate workpiece 3D positioning subsequently.

Description

Technical field [0001] The invention relates to the field of using binocular vision to perform 3D positioning of a workpiece, and specifically refers to an image matching method applied to an industrial field that can effectively solve the problem of shooting the same workpiece under different angles by a binocular vision system. Background technique [0002] Stereo matching is a key technology of the binocular vision system, and its purpose is to determine the correspondence between the points of the stereo image to obtain the disparity map. At present, it can be roughly divided into two categories: global matching methods and local matching methods. Global matching methods mainly include dynamic programming, belief propagation, Graph Cut, etc. These methods obtain dense parallax, which requires a large amount of calculation and takes a long time, and is not suitable for occasions with high real-time requirements. Local matching methods mainly include region-based matching meth...

AI Technical Summary

Problems solved by technology

However, the shape context only considers the position distribution relationship of each point, and ignores the gradient attribute of the point itself. There are one-to-many and many-to-one mismatch problems during matching. For stereo matching, determine the correspondence between stereo image pairs. , the feature description discrimination is not large enough, resulting in the accuracy of point matching cannot meet the requirements of subsequent positioning

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