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Estimating the Regularizing Parameters for Belief Propagation Based Stereo Matching Algorithm  

Oh, Kwang-Hee (Department of Information and Communications Engineering, Hankuk University of Foreign Studies)
Lim, Sun-Young (Department of Information and Communications Engineering, Hankuk University of Foreign Studies)
Hahn, Hee-Il (Department of Information and Communications Engineering, Hankuk University of Foreign Studies)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.47, no.1, 2010 , pp. 112-119 More about this Journal
Abstract
This paper defines the probability models for determining the disparity map given stereo images and derives the methods for solving the problem, which is proven to be equivalent to an energy-based stereo matching. Under the assumptions the difference between the pixel on the left image and the corresponding pixel on the right image and the difference between the disparities of the neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameter is proposed. Usually energy-based stereo matching methods are so sensitive to the parameter that it should be carefully determined. The proposed method alternates between estimating the parameter with the intermediate disparity map and estimating the disparity map with the estimated parameter, after computing it with random initial parameter. It is shown that the parameter estimated by the proposed method converges to the optimum and its performance can be improved significantly by adjusting the parameter and modifying the energy term.
Keywords
Stereo matching; Belief propagation; Markov Random Fields; Parameter estimation;
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1 F. R. Kschischang, B. J. Frey, H. Loeliger, "Factor Graphs and the Sum-Product Algorithm," IEEE Transactions on Information Theory, vol.47, no.2, pp. 498-519, Feb. 2001.   DOI   ScienceOn
2 D. Scharstein and R. Szeliski, "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms," International Journal of Computer Vision, pp.7-42, 2002.
3 J. Sun, N. N. Aheng, and H. Y. Shum, "Stereo Matching Using Belief Propagation," IEEE Transactions on Pattern Analysis and Machine Intelligence ,vol.25, no.7, pp.787-800, July 2003.   DOI   ScienceOn
4 J. S. Yedidia, W. T. Freeman and Y. Weiss, "Understanding Belief Propagation and Its Generalizations," TR-2001-22, Jan. 2002.
5 "Middlebury Stereo Vision Page," http://vision..middlebury.edu/stereo.
6 M. J. Black and A. Rangardjan, "On the Unification of Line Processes, Outlier Rejection, and Robust Statistics with Applications in Early Vision," International Journal of Computer Vision, 1995.
7 P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient Belief Propagation for Early Vision," Proc. Conf. Computer Vision and Pattern Reccognition, pp.261-268, 2004.
8 L. Zhang and S. M. Seitz, "Estimating Optimal Parameters for MRF Stereo from a Single Image Pair," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no.2, pp.331-342, Feb. 2007.