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
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
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
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
Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The conventional technique for scene reconstruction from stereo image pairs searches for the best single surface fitting identified correspondences between the the two images. Constraints on surface continuity, smoothness, and visibility (occlusions) are incorporated into a ‘cost' – usually an ad hoc linear combination of signal similarity criteria, with empirically selected coefficients. An unsatisfactory feature of this approach is that matching accuracy is very sensitive to correct choice of these coefficients. Also, few real scenes have only one surface, so that the single surface assumption contributes to matching errors. We propose a noise-driven paradigm for stereo matching that does not couple the matching process with choice of surfaces by imposing constraints in the matching step. We call our strategy ‘Concurrent Stereo Matching' because the first step involves a high degree of parallelism (making real-time implementations possible using configurable hardware): rather than search for ‘best' matches, it first identifies all 3D volumes that match within a criteria based on noise in the image. Starting in the foreground, these volumes are then examined and surfaces are selected which exhibit high signal similarity in both images. Local constraints on continuity and visibility – rather than global ones – are used to select surfaces from the candidates identified in the first step.