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http://dx.doi.org/10.7471/ikeee.2022.26.4.686

Comparison of error rates of various stereo matching methods for mobile stereo vision systems  

Joo-Young, Lee (Dept. of Electronics Eng.)
Kwang-yeob, Lee (Dept. of Computer Eng., Seokyeong University)
Publication Information
Journal of IKEEE / v.26, no.4, 2022 , pp. 686-692 More about this Journal
Abstract
In this paper, the matching error rates of modified area-based, energy-based algorithms, and learning-based structures were compared for stereo image matching. Census transform (CT) based on region and life propagation (BP) algorithm based on energy were selected, respectively.Existing algorithms have been improved and implemented in an embedded processor environment so that they can be used for stereo image matching in mobile systems. Even in the case of the learning base to be compared, a neural network structure that utilizes small-scale parameters was adopted. To compare the error rates of the three matching methods, Middlebury's Tsukuba was selected as a test image and subdivided into non-occlusion, discontinuous, and disparity error rates for accurate comparison. As a result of the experiment, the error rate of modified CT matching improved by about 11% when compared with the existing algorithm. BP matching was about 87% better than conventional CT in the error rate. Compared to the learning base using neural networks, BP matching was about 31% superior.
Keywords
stereo matching; census transform; belief propagation; neural network disparity map;
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Times Cited By KSCI : 1  (Citation Analysis)
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