Research On A Stereo Matching Algorithm Based On Image Segmentation

Stereo matching is a correspondence between the relations by looking for the same space at different point of view of the pixel under the projection image and eventually get the disparity map of the scene. Stereo matching is the core issue in stereo vision. However, due to deformation, occlusion, texture-less regions the impact of false matches, etc., stereo matching is difficult to obtain high precision disparity map. Therefore, stereo matching is the most difficult part of stereo vision.In this paper, stereo vision technology has been studied, focused on stereo matching algorithm. From the perspective of improving precision of disparity map, an improved stereo matching algorithm based on a framework of Tao has been proposed. Aimed at initial disparity acquirement, the calculation of the plane parameters and selection of the global evaluation function have been improved. And the whole algorithm includes several steps, such as color image segmentation, initial disparity acquirement, segments categories, the calculation of the plane parameters, the plane parameters optimization and so on. For image segmentation, a widely used and relatively good mean-shift algorithm has been adopted. In the initial disparity acquirement of Tao algorithm, deviation smaller window SAD algorithm has been adopted, resulting in texture-less regions more false matches and a subsequent negative impact of plane parameters. This paper based on variable window technique to obtain more initial match points, and the process used in the calculation of consistency checking and the similarity measures filtering to remove false matching points in order to ensure the reliability of the initial matching points. Because there are some regions with less matching points after the segmentation, these regions of the plane parameter are not calculated accurately, the paper calculate plane parameters of regions with more matching points, then use the same or similar plane parameters instead of the less initial matching points regions, and obtained by the plane parameter optimization the final template parameter of unreliable regions. In plane parameters optimization stage, containing data items, smooth items and occlusion items of the evaluation function has been adopted, with occlusion constraints.This paper also uses VC6.0 development tools to build on the PC, the software system platform for the underlying algorithm in the experiment. The experiments results show our algorithm has a higher matching accuracy, the boundary clear and more accurate positioning and disparity map of texture-less regions has also been well restored.