Robust ellipse detection via duality principle with a false determination control

In this paper, we propose a novel ellipse detection approach that eliminates false detection-based parameter space decomposition, principal of symmetric tangents, and a novel geometric constraint utilizing properties of tangents of ellipses. The principle of symmetric tangents provides better comput...

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Main Authors: Dong, Huixu, Chen, I-Ming, Prasad, Dilip Kumar
Other Authors: School of Computer Science and Engineering
Format: Conference or Workshop Item
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/140450
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1404502020-05-29T04:06:40Z Robust ellipse detection via duality principle with a false determination control Dong, Huixu Chen, I-Ming Prasad, Dilip Kumar School of Computer Science and Engineering Proceedings of 2nd International Conference on Computer Vision & Image Processing (CVIP 2017) Robotics Research Centre Engineering::Computer science and engineering Ellipse Detection Principle of Geometric Duality In this paper, we propose a novel ellipse detection approach that eliminates false detection-based parameter space decomposition, principal of symmetric tangents, and a novel geometric constraint utilizing properties of tangents of ellipses. The principle of symmetric tangents provides better computational efficiency through confirmation of the ellipse center in the decomposed parameter space. The geometric constraint is used for alleviating the false detection probability. The experimental results confirm that the approach detects ellipse with an excellent accuracy at a high speed. 2020-05-29T04:06:39Z 2020-05-29T04:06:39Z 2018 Conference Paper Dong, H., Chen, I.-M., & Prasad, D. K. (2018). Robust ellipse detection via duality principle with a false determination control. Proceedings of 2nd International Conference on Computer Vision & Image Processing, 2, 223-234. doi:10.1007/978-981-10-7898-9_18 9789811078972 https://hdl.handle.net/10356/140450 10.1007/978-981-10-7898-9_18 2-s2.0-85047947325 2 223 234 en © 2018 Springer Nature Singapore Pte Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Ellipse Detection
Principle of Geometric Duality
spellingShingle Engineering::Computer science and engineering
Ellipse Detection
Principle of Geometric Duality
Dong, Huixu
Chen, I-Ming
Prasad, Dilip Kumar
Robust ellipse detection via duality principle with a false determination control
description In this paper, we propose a novel ellipse detection approach that eliminates false detection-based parameter space decomposition, principal of symmetric tangents, and a novel geometric constraint utilizing properties of tangents of ellipses. The principle of symmetric tangents provides better computational efficiency through confirmation of the ellipse center in the decomposed parameter space. The geometric constraint is used for alleviating the false detection probability. The experimental results confirm that the approach detects ellipse with an excellent accuracy at a high speed.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Dong, Huixu
Chen, I-Ming
Prasad, Dilip Kumar
format Conference or Workshop Item
author Dong, Huixu
Chen, I-Ming
Prasad, Dilip Kumar
author_sort Dong, Huixu
title Robust ellipse detection via duality principle with a false determination control
title_short Robust ellipse detection via duality principle with a false determination control
title_full Robust ellipse detection via duality principle with a false determination control
title_fullStr Robust ellipse detection via duality principle with a false determination control
title_full_unstemmed Robust ellipse detection via duality principle with a false determination control
title_sort robust ellipse detection via duality principle with a false determination control
publishDate 2020
url https://hdl.handle.net/10356/140450
_version_ 1681056400503996416