关键词: 图像处理, 立体匹配, 高斯混合模型, 最小生成树, 视差求精

Abstract: As traditional stereo matching algorithms cannot provide an appropriate size aggregate window for image edges and low texture regions at the same time, an improved stereo matching algorithm is proposed, which is based on Gaussian mixture model and minimum spanning tree structure. The image is divided into the several initial regions and the candidate pixels to be segmented as the first step, which is obtained by the pixel color and the distance information, together with the initial disparity. The Gaussian mixture model, which is run in parallel, is secondly leveraged to get the final segmentation by updating the parameters of each region iteratively. Then, a minimum spanning tree is built on each segment to obtain the disparity. Finally, a high precision dense disparity map is obtained by correcting the mismatch of valid disparity from the neighbors. Compared to other algorithms proposed in the literature, this algorithm provides substantial precision improvement, especially in the depth discontinuity region.

Key words: image processing, stereo matching, Gaussian mixture model, minimum spanning tree, disparity refinement