ISSN : 1796-2048
Volume : 1    Issue : 7    Date : November/December 2006

Integrating BC & GC Models In Computing Stereo Disparity As Markov Random Field
Hongsheng Zhang and Shahriar Negahdaripour
Page(s): 30-39
Full Text:
PDF (1,417 KB)

Belief propagation and graph cuts have emerged as powerful tools for computing efficient
approximate solution to stereo disparity field modelled as the Markov random field (MRF). These
algorithms have provided the best performance based on results on a standard data set. However,
employment of the brightness constancy (BC)  assumption severely limits the range of their
applications. Previously, augmenting the BC with gradient constancy (GC) assumption has shown
to produce a more robust optical flow algorithm. In this paper, these constraints are integrated
within the MRF framework to devise an enhanced global method that broadens the application
domains for stereo computation. Results of experiments with both semi-synthetic data and more
challenging ocean images are presented to illustrate that the proposed method generally
outperforms earlier dense optical flow and stereo algorithms.

Index Terms
Stereo disparity, brightness constancy model, gradient constancy model, Markov random field,
multiresolution/multi-grid, belief propagation, graph cuts