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Summary: EFFICIENT BP STEREO WITH AUTOMATIC PARAMEMETER ESTIMATION
Shafik Huq, Andreas Koschan, Besma Abidi, and Mongi Abidi
Department of Electrical Engineering and Computer Science
University of Tennessee at Knoxville, TN, USA
ABSTRACT
In this paper, we propose a series of techniques to enhance
the computational performance of existing Belief
Propagation (BP) based stereo matching that relies on
automatic estimation of the Markov random field (MRF)
parameters. First, we show how convergence in matching
can be achieved faster than with the existing message
comparison technique by skipping comparisons in early
inferences. Second, assuming that a stereo pair is captured
with identical cameras, we apply a hypothesis called noise
equivalence to pre-estimate the likelihood parameters and
thus, avoid costly nested inferences to reduce the
computational time. The likelihood parameters and intensity
information are used for accelerated message propagation in
image regions lacking gradients. Third, the prior model
parameters are estimated with a combination of maximum
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