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Summary: MRF Stereo with Statistical Parameter Estimation
Shafik Huq, Andreas Koschan, Besma Abidi, and Mongi Abidi
Min H. Kao Department of Electrical Engineering and Computer Science
University of Tennessee at Knoxville, TN, USA
Abstract
A Markov Random Field (MRF) based local stereo
matching algorithm that estimates parameters
automatically from statistics is proposed. For an iterative
optimization, cost functions working on local support
neighborhood are developed. Data model parameters are
pre-estimated from one of the stereo images by applying a
noise equivalence hypothesis. The smoothness model
parameters are estimated with maximum likelihood (ML)
applying disparity gradient constraint and 3*sigma
confidence boundary. The confidence boundary also
defines the parameters for handling discontinuities in data
and smoothness. Additionally, homogeneous points are
included into the support neighborhood to achieve high
matching rate along surface borders. Finally, a pair of
cost functions is modeled to match the images
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