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8 October 2015 A stereo matching handling model in low-texture region
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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 967538 (2015)
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
In binocular stereo matching, mistakes are relatively easy to appear in low-texture region due to the weak detail information. In order to eliminate the matching ambiguity as well as guarantee the matching rate, this paper proposes a stereo matching algorithm based on image segmentation. In most low-texture region, traditional cost functions are usually used, and the algorithm can only ameliorated through methods such as reasonable support window, dynamic programming and so on. The results of these algorithms make the whole image smooth, and lose many details. The matching cost function in our algorithm is based on the assumption that pixels are similar in homogeneous area, and reduce the use of multiplication so as to obtain better visual effects and decrease the computational complexity. The first is forming the segmentation maps of stereoscopic images as the guidance. Next comes calculating the aggregation cost in stereo matching in both horizontal and vertical direction successively referring to the segmentation maps. Eventually achieving the final disparity map with optimization algorithm, using WTA(Winner-Takes-All) as principle. The computational complexity of this algorithm is independent of the window size, and suitable for different sizes and shapes. The results of experimental show that this algorithm can get better matching precision about the colorful low-texture stereo image pairs, with few increase in computational complexity. This algorithm, to some extent, can improve the match quality of the regions with repeat texture.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yi Ma, Yi Zhang, Jing Han, and Lianfa Bai "A stereo matching handling model in low-texture region", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967538 (8 October 2015);

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