An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight

Ingyu Lee and Byungin Moon
Volume: 13, No: 2, Page: 256 ~ 267, Year: 2017
10.3745/JIPS.02.0057
Keywords: Adaptive Census Transform, Adaptive Support Weight, Local Matching, Multiple Sparse Windows, Stereo Matching
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Abstract
An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

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Cite this article
IEEE Style
Ingyu Lee and Byungin Moon, "An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight ," Journal of Information Processing Systems, vol. 13, no. 2, pp. 256~267, 2017. DOI: 10.3745/JIPS.02.0057.

ACM Style
Ingyu Lee and Byungin Moon, "An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight ," Journal of Information Processing Systems, 13, 2, (2017), 256~267. DOI: 10.3745/JIPS.02.0057.