基于区域间协同优化的立体匹配算法

提出了一种基于分割区域间协同优化的立体匹配算法. 该算法以图像区域为匹配基元, 利用区域的彩色特征以及相邻区域间应满足的平滑和遮挡关系定义了区域的匹配能量函数, 并引入区域之间的合作竞争机制, 通过协同优化使所定义的匹配能量极小化, 从而得到比较理想的视差结果. 算法首先对参考图像进行分割, 利用相关法得到各分割区域的初始匹配; 然后用平面模型对各区域的视差进行拟合, 得到各区域的视差平面参数; 最后, 基于协同优化的思想, 采用局部优化的方法对各区域的视差平面参数进行迭代优化, 直至得到比较合理的视差图为止. 采用Middlebury test set进行的实验结果表明, 该方法在性能上可以和目前最好的立体匹配算法相媲美, 得到的视差结果接近于真实视差.

A Region Based Stereo Matching Algorithm Using Cooperative Optimization

This paper presents a stereo matching algorithm based on inter-regional cooperative optimization. This algorithm uses regions as matching primitives and defines the corresponding region cost functions for matching by utilizing the color statistics of regions and the constraints on smoothness and occlusion between adjacent regions. In order to obtain a more reasonable disparity map, a cooperative optimization procedure is employed to minimize the matching costs of all regions by introducing the cooperative and competitive mechanism between regions. Firstly, a color based segmentation method is used to segment the reference image into regions with homogeneous color. Secondly, a local window-based matching method is used to determine the initial disparity estimates of each image point. And then, a plane fitting technique is applied to obtain the parameters of disparity plane corresponding to each image region. Finally, under a framework of inter-regional cooperative optimization, the disparity plane parameters of all regions are iteratively optimized by a local optimization method until a reasonable disparity map is obtained. The experimental results based on Middlebury test set indicate that the performance of our method is competitive with the best stereo matching algorithms and the disparity maps recovered are close to the ground truth data.