Computation of stereo disparity using regularization
Pattern Recognition Letters
A Bayesian approach to binocular stereopsis
International Journal of Computer Vision
Stereo Matching with Nonlinear Diffusion
International Journal of Computer Vision
International Journal of Computer Vision
A Theory of Shape by Space Carving
International Journal of Computer Vision - Special issue on Genomic Signal Processing
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occlusions, Discontinuities, and Epipolar Lines in Stereo
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
EYESCAN - A High Resolution Digital Panoramic Camera
RobVis '01 Proceedings of the International Workshop on Robot Vision
Disparity Component Matching for Visual Correspondence
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Distinctiveness Maps for Image Matching
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
SMBV '01 Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01)
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Stereo Depth Estimation: A Confidence Interval Approach
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Towards automatic visual obstacle avoidance
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Fast Unambiguous Stereo Matching Using Reliability-Based Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Using Monocular Cues within the Tensor Voting Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense 3D reconstruction from images by normal aided matching
Machine Graphics & Vision International Journal
Search Space Reduction for MRF Stereo
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Coarse-to-fine stereo vision with accurate 3D boundaries
Image and Vision Computing
Dense stereomatching algorithm performance for view prediction and structure reconstruction
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Graph-based range image registration combining geometric and photometric features
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Reliable depth map regeneration via a novel omnidirectional stereo sensor
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Efficient large-scale stereo matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Extracting dense features for visual correspondence with graph cuts
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Region growing stereo matching method for 3D building reconstruction
International Journal of Computational Vision and Robotics
Review article: A 1D approach to correlation-based stereo matching
Image and Vision Computing
Fast and robust semi-local stereo matching using possibility distributions
International Journal of Computational Vision and Robotics
A semi-automatic 3d reconstruction algorithm for telepresence
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
Complex correlation statistic for dense stereoscopic matching
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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Stereo matching is an ill-posed problem for at least two principal reasons: (1) because of the random nature of match similarity measure and (2) because of structural ambiguity due to repetitive patterns. Both ambiguities require the problem to be posed in the regularization framework. Continuity is a natural choice for a prior model. But this model may fail in low signal-to-noise ratio regions. The resulting artefacts may then completely spoil the subsequent visual task.A question arises whether one could (1) find the unambiguous component of matching and, simultaneously, (2) identify the ambiguous component of the solution and then, optionally, (3) regularize the taskfor the ambiguous component only. Some authors have already taken this view. In this paper we define a new stability property which is a condition a set of matches must satisfy to be considered unambiguous at a given confidence level. It turns out that for a given matching problem this set is (1) unique and (2) it is already a matching. We give a fast algorithm that is able to find the largest stable matching. The algorithm is then used to show on real scenes that the unambiguous component is quite dense (10-80%) and error-free (total error rate of 0.3-1.4%), both depending on the confidence level chosen.