基于凸集论的稠密视差图优化算法

针对计算机视觉领域中立体匹配算法在图像无纹理区产生的误匹配问题,提出以全变差函数为凸约束集,使用次梯度投影法,对通过初始视差变换后的立体图像对之间的像素误差函数和视差二次平滑因子形成的严格凸函数进行求解,寻求立体图像对之间的最优视差图。实验结果表明,该方法不仅能保留初始视差图的边缘,而且减少了由先前立体匹配算法求得的视差图在无纹理区产生的误匹配点,取得了良好的视差图优化效果。

Dense disparity map optimization algorithm based on convex set theory

According to the mismatch problem in less textured area of stereo match algorithms in computer vision field,in order to find the optimal disparity map of the stereo image pair,a method using the sub-gradient projection method to solve strict convex function which composed by the pixel error function of the shaped image pairs and quadratic smoothing factor of disparity map was proposed,which was restrained by the convex constraint set of total variation function.Experimental results show that the method can keep the edge of primal disparity map well,reduce the mismatch in the less textured area of disparity generated by primal stereo matching algorithm,and get the better optimization disparity map effectively.