Estimating Disparity and Occlusions in Stereo Video Sequences
Oliver Williams, Michael Isard and John MacCormick
Proc Computer Vision and Pattern Recognition, (II) 250-257 (2005)
Abstract
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 --- scene points
visible in one camera but not the other --- 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.
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