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CCDA: a concise corner detection algorithm

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Abstract

In this article, the authors propose a concise corner detection algorithm, which is called CCDA. A cascade classifier concept is used to derive a corner detector, which can quickly discard the most non-corner pixels. The ruler of gradient direction is used to get the corner, which can avoid the influence of the light change. The method of second derivative non-maximum suppression is used to get the location of the corner and can get the exact corner point. As a result, CCDA is compare-tested with classical corner detection algorithms by using the same images which include synthetic corner patterns and real images. The result shows that CCDA has a similar speed to the FAST algorithm and better accuracy and robustness than the HARRIS algorithm.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (41761087) and Guangxi Natural Science Foundation (2017GXNSFAA198162), by Foundation of Guangxi Experiment Center of Information Science (YB1414), by Innovation Project of Guangxi Graduate Education (YCBZ2017051), by Guangxi College’s emphasis laboratory foster base for optoelectronics information (handling) Project (GD18108), and by the study abroad program for graduate student of Guilin University of Electronic Technology.

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Authors and Affiliations

  1. College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004, China

    Zhiyong Peng & Jun Wu

  2. Guangxi Experiment Center of Information Science, Guilin, 541004, China

    Zhiyong Peng

  3. Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin, 541004, China

    Zhiyong Peng

  4. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma, 74074, USA

    Guoliang Fan

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  1. Zhiyong Peng
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Correspondence to Jun Wu.

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Peng, Z., Wu, J. & Fan, G. CCDA: a concise corner detection algorithm. Machine Vision and Applications 30, 1029–1040 (2019). https://doi.org/10.1007/s00138-019-01035-7

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