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American Journal of Computer Science and Information Engineering

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Performance Review of the Stereo Matching Algorithms

American Journal of Computer Science and Information Engineering
Vol.4 , No. 1, Publication Date: Jun. 13, 2017, Page: 7-15

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Authors

[1]	Md. Abdul Mannan Mondal, Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh.
[2]	Md. Haider Ali, Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh.

Abstract

Stereo correspondence or disparity is a common tool in computer or robotic vision, essential for determining three-dimensional depth information of object using a pair of left and right images from a stereo camera system. Some applications of disparity are autonomous vehicle and robot navigation, virtual reality and stereo image analysis. Stereo correspondence or disparity is determined of matching windows of pixels by using Sum of Square Differences (SSD), Sum of Absolute Differences (SAD), or normalized correlation techniques. As a result to identify the problem of matching pixels between two images of a stereo pair several algorithms have been invented in the respective arena. In this paper comparative performance analysis of existing stereo matching algorithms are explored detailed up to date. The methods that are considered in the paper are classified into two categories. First one is named as local method while the second one is global method. The algorithms taken consideration in literature are analyzed by speed, accuracy and disparity range. Experimental results applied on different image sizes and different image sets (Tsukuba Stereo pair, Sawtooth stereo pair, Map Stereo pair and Venus Stereo pair) are also presented. Some neural network and automata based latest algorithms are discussed. Besides these some algorithms are not fallen into above mentioned categories are also discussed in details.

Keywords

Stereo Correspondence, Disparity, Sum of Absolute Difference, Sum of Square Difference, Normalize Correlation, Dense Disparity, Stereo Vision

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