An accurate stereo matching method based on color segments and edges

Stereo matching algorithms of binocular vision suffer from low accuracy when dealing with natural scenes (such as industrial robot scenes). Biological vision is sensitive to object edges; it divides objects by their edges, and then perceives their distances. Similar to the biological eye mechanism, this study proposes a matching algorithm that combines segment- and edge-matching to obtain the disparity. In segment matching, pixel strings from the same row of the left and right images are divided into pixel segments, whose colors and lengths are used as clues to determine several types of matching pixel segment pairs according to non-crossing mapping. The analysis of the spatial state yields several types of stimulus bars. Disparities can be obtained from the relation between pixel segment pairs and stimulus bars. In edge matching, the DTW (Dynamic Time Warping) algorithm and the gradient are used to determine the initial edge pixel matching results. The remaining edge point disparity is obtained by fitting a fill to the existing edge point disparity. Finally, segment and edge matching results are combined to check and fill and post-processing. This new matching method transforms pixel matching to pixel segment matching and edge matching, which can reduces the time complexity. The algorithm can be implemented in an industrial robot environment for high-precision needle threading guidance, which neither traditional binocular matching nor deep learning matching algorithms can do.