Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Estimation of Motion Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occlusions and binocular stereo
International Journal of Computer Vision
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
A Probabilistic Approach to the Coupled Reconstruction and Restoration of Underwater Acoustic Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A compact algorithm for rectification of stereo pairs
Machine Vision and Applications
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Local Transforms for Computing Visual Correspondence
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Disparity-Space Images and Large Occlusion Stereo
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Efficient Stereo with Multiple Windowing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Visual Integration and Detection of Discontinuities: The Key Role of Intensity Edges
Visual Integration and Detection of Discontinuities: The Key Role of Intensity Edges
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
3D face recognition using stereoscopic vision
ASB'03 Proceedings of the 1st international conference on Advanced Studies in Biometrics
Optimal parameter estimation for MRF stereo matching
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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This paper introduces R-SMW, a new algorithm for stereo matching. The main aspect is the introduction of a Markov Random Field (MRF) model in the Symmetric Multiple Windows (SMW) stereo algorithm in order to obtain a non-deterministic relaxation. The SMW algorithm is an adaptive, multiple window scheme using left-right consistency to compute disparity. The MRF approach allows to combine in a single functional the disparity values coming from different windows, the left-right consistency constraint and regularization hypotheses. The optimal estimate of the disparity is obtained by minimizing an energy functional with simulated annealing. Results with both synthetic and real stereo pairs demonstrate the improvement over the original SMW algorithm, which was already proven to perform better than state-of-the-art algorithms.