NDLI: Cost aggregation table: A theoretic derivation on the Markov random field and its relation to message passing

Content Provider	IEEE Xplore Digital Library
Author	Jeong Mok Ha Byeongchan Jeon JeaYoung Jeon Sung Yong Jo Hong Jeong
Copyright Year	2015
Description	Author affiliation: Electr. Eng., Pohang Univ. of Sci. & Technol., Pohang, South Korea (Jeong Mok Ha; Byeongchan Jeon; JeaYoung Jeon; Sung Yong Jo; Hong Jeong)
Abstract	The cost aggregation table (CAT) algorithm is a cost aggregation method for stereo matching that combines the ideas of the summed area table and semi-global matching (SGM). For the same computational complexity, this method generates more accurate disparity results than SGM, which was the most efficient stereo matching method for a decade. However, it has not been explained theoretically why CAT generates more accurate disparity results than SGM even though they have the same computational complexity. In this paper, we address the theoretic derivation of CAT based on Markov random fields (MRFs). The reason that CAT gives better disparity results than SGM is proven to be an aspect of energy minimization on the factor graph. In addition, we show that the origin of CAT is in message passing-based algorithms by showing that SGM, CAT, and belief propagation are all related to each other on different graph structures. We hope that this work will be the start of the generalization of CAT as a solution to labeling problems.
Starting Page	2224
Ending Page	2228
File Size	679979
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision support systems message passing Markov random field labeling problem cost aggregation table semi-global matching
Content Type	Text
Resource Type	Article

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