Performance Comparison of Max Product and Minimum Sum Belief Propagation
Abstract
The stereo pair image consists of two images of same scene or object taken slightly horizontally separated points from left and right view. The phenomena of parallax effect present in stereo pair image due to above shift object near to the camera appears more to the right in left image whereas more left in right image. The stereo matching / stereo correspondence technique, finds the horizontal matching pixel in stereo pair is known as disparity whereas for each pixel coordinates is’ Disparity Map’ .There are local and global methods to find ‘Disparity Map’. The global method is based on Bayesian approach which computes ‘Disparity Map’ as minimum energy formulation.
To optimize energy minimization formulation, “Belief Propagation (BP)†method is one of many available inference methods which can be used efficiently. The Belief Propagation (BP) method is based on probability theory ,finds beliefs or marginal probability by passing the messages around possible unknown disparity values. The beliefs or marginal probability can be evaluated either by Maximum A Posteriori (MAP) assignment or using log space. The beliefs or marginal probability findings based on Maximum A Posteriori(MAP) estimation is known as Max product Belief Propagation(BP) whereas based on log space is known as Minimum Sum Belief Propagation(BP).
In this paper we are comparing and analyzing computational parameters of Mean Square Error per pixel and Peak signal Noise Ratio in dB of Disparity Map using Max product Belief Propagation (BP) as well as by Minimum Sum Belief Propagation (BP).Further runtime also analyzed and objects in stereo pair due to depth discontinuities are detected efficiently by Minimum Sum Belief Propagation
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