MEMBER, IEEE, Department of Computer Science, University of Minnesota, Minneapolis, MN 55455; SRI International, Menlo Park, CA 94025.
IEEE Trans Pattern Anal Mach Intell. 1980 Apr;2(4):333-40. doi: 10.1109/tpami.1980.4767032.
An algorithm for matching images of real world scenes is presented. The matching is a specification of the geometrical disparity between the images and may be used to partially reconstruct the three-dimensional structure of the scene. Sets of candidate matching points are selected independently in each image. These points are the locations of small, distinct features which are likely to be detectable in both images. An initial network of possible matches between the two sets of candidates is constructed. Each possible match specifies a possible disparity of a candidate point in a selected reference image. An initial estimate of the probability of each possible disparity is made, based on the similarity of subimages surrounding the points. These estimates are iteratively improved by a relaxation labeling technique making use of the local continuity property of disparity that is a consequence of the continuity of real world surfaces. The algorithm is effective for binocular parallax, motion parallax, and object motion. It quickly converges to good estimates of disparity, which reflect the spatial organization of the scene.
提出了一种匹配真实场景图像的算法。匹配是对图像之间的几何视差的规定，可用于部分重建场景的三维结构。在每个图像中独立选择候选匹配点集。这些点是小而独特的特征的位置，这些特征在两幅图像中都可能被检测到。构建了两个候选点集之间的初始可能匹配网络。每个可能的匹配指定所选参考图像中候选点的可能视差。基于点周围子图像的相似性，对每个可能视差的初始概率进行估计。这些估计通过利用视差的局部连续性特性的松弛标记技术进行迭代改进，这是真实世界表面连续性的结果。该算法对视差有效，适用于双目视差、运动视差和物体运动。它可以快速收敛到良好的视差估计值，这些估计值反映了场景的空间组织。
