Issue No. 11 - November (1989 vol. 11)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.42855
<p>The authors propose a method for solving the stereo correspondence problem. The method consists of extracting local image structures and matching similar such structures between two images. Linear edge segments are extracted from both the left and right images. Each segment is characterized by its position and orientation in the image as well as its relationships with the nearby segments. A relational graph is thus built from each image. For each segment in one image as set of potential assignments is represented as a set of nodes in a correspondence graph. Arcs in the graph represent compatible assignments established on the basis of segment relationships. Stereo matching becomes equivalent to searching for sets of mutually compatible nodes in this graph. Sets are found by looking for maximal cliques. The maximal clique best suited to represent a stereo correspondence is selected using a benefit function. Numerous results obtained with this method are shown.</p>
image structure extraction; segmentation; stereo matching; pattern recognition; graph theory; picture processing; feature grouping; maximal cliques; stereo correspondence; relational graph; nodes; graph theory; pattern recognition; picture processing
T. Skordas and R. Horaud, "Stereo Correspondence Through Feature Grouping and Maximal Cliques," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 11, no. , pp. 1168-1180, 1989.