Shape Context Stereo Matching Method Based on Texture Features

In binocular vision measurement systems, image matching is an indispensable part. However, in the measurement of simple workpieces, due to the high similarity of the workpiece edges and the complicated distribution, it is often difficult to match. The traditional shape context matching algorithm only considers the position distribution of contour points, ignoring its own gradient features, which not only causes a high mismatch rate, but also is computationally complex and takes a long time. Therefore, based on the texture features at the edge contour points and the joint polar line constraint, an improved shape context matching algorithm is proposed. On the one hand, the DLBP (Double Local Binary Pattern) texture feature weight value is introduced, and the edge point gradient information is used to make the algorithm more robust to noise and illumination changes, and reduce the mismatch rate. On the other hand, by combining the polar constraint, the search space is limited to a small rectangular frame, which reduces the computational time and further reduces the mismatch rate. Finally, the effectiveness and practicability of the proposed algorithm are demonstrated by experiments.