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
A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Without Epipolar Lines: A Maximum-Flow Formulation
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Shape by Space Carving
International Journal of Computer Vision - Special issue on Genomic Signal Processing
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic regularisation and symmetry in binocular dynamic programming stereo
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Correspondence Using Energy Minimization and Mutual Information
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Symmetric Stereo Matching for Occlusion Handling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Most published techniques for reconstructing scenes from stereo pairs follow a conventional strategy of searching for a single surface yielding the best correspondence between the images. The search involves specific constraints on surface continuity, smoothness, and visibility (occlusions) embedded in a matching score – typically an ad hoc linear combination of distinctly different criteria of signal similarity. The coefficients or weighing factors are selected empirically because they dramatically effect accuracy of stereo matching. The single surface assumption is also too restrictive – few real scenes have only one surface. We introduce a paradigm of concurrent stereo that circumvents in part these problems by separating image matching from a choice of the 3D surfaces. Concurrent stereo matching first detects all likely matching 3D volumes instead of single best matches. Then, starting in the foreground, the volumes are explored, selecting mutually consistent optical surfaces that exhibit high point-wise signal similarity. Local, rather than global, surface continuity and visibility constraints are applied.