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Corner detection using arc length-based angle estimator

[+] Author Affiliations
Shizheng Zhang, Sheng Huang, Ying Qu, Liyun Tu

Chongqing University, College of Computer Science, Shapingba District, Chongqing 400044, China

Dan Yang

Chongqing University, College of Computer Science, Shapingba District, Chongqing 400044, China

Chongqing University, School of Software Engineering, Shapingba District, Chongqing 400044, China

Xiaohong Zhang

Chongqing University, School of Software Engineering, Shapingba District, Chongqing 400044, China

Zemin Ren

Chongqing University of Science and Technology, College of Mathematics and Physics, Shapingba District, Chongqing 401331, China

J. Electron. Imaging. 24(6), 063010 (Nov 30, 2015). doi:10.1117/1.JEI.24.6.063010
History: Received May 22, 2015; Accepted October 26, 2015
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Abstract.  We present a corner-detection method named arc length-based angle estimator (AAE). Different from most of the existing approaches, AAE focuses on employing angle detection for finding corners, because angle is an important measure for discrete curvature. AAE provides a new robust solution to the estimation of the K-cosine. In AAE, the K-cosine estimation issue in the x, y space is considered as the problem of the slope estimations in the s, x and s, y spaces, where s is the arc length. Then, weighted least square fitting is employed to address such a slope estimation issue. Experimental results demonstrate that AAE can achieve promising performance in comparison with some recent state-of-the-art approaches under two commonly used evaluation metrics, namely average repeatability and localization error criteria.

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Citation

Shizheng Zhang ; Dan Yang ; Sheng Huang ; Xiaohong Zhang ; Ying Qu, et al.
"Corner detection using arc length-based angle estimator", J. Electron. Imaging. 24(6), 063010 (Nov 30, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.063010


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