Probabilistic Fusion of Stereo with Color and Contrast for Bilayer Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new stereo matching model using visibility constraint based on disparity consistency
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Dense motion and disparity estimation via loopy belief propagation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Temporally consistent disparity and optical flow via efficient spatio-temporal filtering
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
On the evaluation of scene flow estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Quasi-Parallax for Nearly Parallel Frontal Eyes
International Journal of Computer Vision
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We propose an algorithm for estimating disparity and occlusion in stereo video sequences. The algorithm defines a prior on sequences of disparity maps using a 3D Markov random field, and approximately computes the MAP estimate for the disparity sequence using loopy belief propagation. In contrast to previous work on temporal stereo, the algorithm (i) correctly models half-occlusions 驴 scene points visible in one camera but not the other 驴 and (ii) enforces the so-called "monotonicity constraint" on the boundary of half-occluded regions. The algorithm is also able to exploit temporal coherence more appropriately than many previous approaches to temporal stereo, by employing additional states in the Markov random field. These additional states permit rudimentary motion estimation to be performed as part of the belief propagation, thus improving the quality of temporal inference. Parameters of the algorithm are learned from the ground truth disparities of a real stereo sequence. Qualitative results are shown on real sequences, including comparisons with competing approaches, and the performance of the algorithm is assessed quantitatively using the ground truth data.