Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of Scene Registration and Stereo Matching for Artographic Feature Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
ACM Computing Surveys (CSUR)
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This paper presents a stereo matcher inspired by the earlier work of Marr and Poggio [7]. Two major extensions are introduced: the algorithm is extended to gray-level images, and the inhibitory/excitatory weights of the model are learned rather than set a priori according to 驴uniqueness驴 and 驴continuity驴 constraints. Gray level stereo pairs of real scenes with known disparity maps are used to train the model. The trained system is successfully tested on other gray level stereo pairs of real scenes as well as a set of random dot stereograms (RDS). Performance is compared to a recent stereo matching algorithm.