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Fast ellipse detection based on three point algorithm with edge angle information

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Abstract

In this paper, we introduce a fast ellipse detection method that uses the geometric properties of three points on an ellipse. Many conventional ellipse detection methods carry out detection using five points, but a random selection of such points among candidate edges requires much redundant processing. To search for an ellipse with the minimum number of points, this study used the normal and differential equations of an ellipse, which requires three points based on their locations and edge angles. First, to reduce the number of candidate edges, the edges were divided into 8 groups depending on the edge angle, and then a new geometric constraint called the quadrant condition was introduced to reduce noisy candidate edges. Clustering was employed to find prominent candidates in the space of a few ellipse parameters. Experiments using many real images showed that the proposed method satisfies both reliability and computing speed for ellipse detection.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, Korea

    Bae-Keun Kwon & Dong-Joong Kang

  2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Zhu Teng

  3. Department of Mechatronis, Engineering in Tongmyong University, Busan, Korea

    Tae-Jung Roh

Authors
  1. Bae-Keun Kwon
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  2. Zhu Teng
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  3. Tae-Jung Roh
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  4. Dong-Joong Kang
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Corresponding author

Correspondence to Dong-Joong Kang.

Additional information

Recommended by Associate Editor Hauping Liu under the direction of Editor Euntai Kim. This work was supported by a 2-Year Research Grant of Pusan National University.

Bae-Keun Kwon received his B.S. degree from School of Mechanical Engineering at Pusan National University, Pusan, Korea in 2008. He received his combined MS and Ph.D. degree from same school of PNU. Now he is a research engineer in LG electronics. His research interests include pattern recognition, image processing and object detection.

Zhu Teng received her B.S. degree in Automation from Central South university, China, in 2006 and a combined MS and Ph.D. degree in Mechanical Engineering from Pusan National University, Pusan, Korea, in 2013. She is now an assistant professor in Beijing Jiaotong University, China. Her current research interests are shape recognition, machine vision, and pattern recognition.

Tae-Jung Roh received his M.S. and Ph.D. degrees in Mechanical Engineering from KAIST, Korea, in 1986 and 1995, respectively. Now he is a professor at the Dept. of Mechatronics Engineering in Tongmyong University. His current research interests are mechatronics and factory automation.

Dong-Joong Kang received his BS degree in Precision Engineering from Pusan National University, Pusan, Korea, in 1988, and his MS and Ph.D. degrees in Mechanical, and Automation & Design Engineering from KAIST, Korea, in 1990 and 1999, respectively. In 1997 to 2000, he was a research engineer at SAIT (Samsung Advanced Institute of Technology). Now he is a professor at the School of Mechanical Engineering in Pusan National University. He is also an associate editor of the International Journal of Control, Automation, and Systems since 2007. His current research interests are machine vision, pattern recognition, and visual inspection in factory.

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Kwon, BK., Teng, Z., Roh, TJ. et al. Fast ellipse detection based on three point algorithm with edge angle information. Int. J. Control Autom. Syst. 14, 804–813 (2016). https://doi.org/10.1007/s12555-014-0561-y

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  • DOI: https://doi.org/10.1007/s12555-014-0561-y

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