Optimizing Region of Support for Boundary-Based Corner Detection: A Statistic Approach

Wen-Bing HORNG; Chun-Wen CHEN

doi:10.1587/transinf.E92.D.2103

Optimizing Region of Support for Boundary-Based Corner Detection: A Statistic Approach

Wen-Bing HORNG, Chun-Wen CHEN

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Boundary-based corner detection has been widely applied in spline curve fitting, automated optical inspection, image segmentation, object recognition, etc. In order to obtain good results, users usually need to adjust the length of region of support to resist zigzags due to quantization and random noise on digital boundaries. To automatically determine the length of region of support for corner detection, Teh-Chin and Guru-Dinesh presented adaptive approaches based on some local properties of boundary points. However, these local-property based approaches are sensitive to noise. In this paper, we propose a new approach to find the optimum length of region of support for corner detection based on a statistic discriminant criterion. Since our approach is based on the global perspective of all boundary points, rather than the local properties of some points, the experiments show that the determined length of region of support increases as the noise intensity strengthens. In addition, the detected corners based on the optimum length of region of support are consistent with human experts' judgment, even for noisy boundaries.

Publication: IEICE TRANSACTIONS on Information Vol.E92-D No.10 pp.2103-2111

Publication Date: 2009/10/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E92.D.2103

Type of Manuscript: PAPER

Category: Pattern Recognition

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Wen-Bing HORNG, Chun-Wen CHEN, "Optimizing Region of Support for Boundary-Based Corner Detection: A Statistic Approach" in IEICE TRANSACTIONS on Information, vol. E92-D, no. 10, pp. 2103-2111, October 2009, doi: 10.1587/transinf.E92.D.2103.
Abstract: Boundary-based corner detection has been widely applied in spline curve fitting, automated optical inspection, image segmentation, object recognition, etc. In order to obtain good results, users usually need to adjust the length of region of support to resist zigzags due to quantization and random noise on digital boundaries. To automatically determine the length of region of support for corner detection, Teh-Chin and Guru-Dinesh presented adaptive approaches based on some local properties of boundary points. However, these local-property based approaches are sensitive to noise. In this paper, we propose a new approach to find the optimum length of region of support for corner detection based on a statistic discriminant criterion. Since our approach is based on the global perspective of all boundary points, rather than the local properties of some points, the experiments show that the determined length of region of support increases as the noise intensity strengthens. In addition, the detected corners based on the optimum length of region of support are consistent with human experts' judgment, even for noisy boundaries.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2103/_p

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@ARTICLE{e92-d_10_2103,
author={Wen-Bing HORNG, Chun-Wen CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Optimizing Region of Support for Boundary-Based Corner Detection: A Statistic Approach},
year={2009},
volume={E92-D},
number={10},
pages={2103-2111},
abstract={Boundary-based corner detection has been widely applied in spline curve fitting, automated optical inspection, image segmentation, object recognition, etc. In order to obtain good results, users usually need to adjust the length of region of support to resist zigzags due to quantization and random noise on digital boundaries. To automatically determine the length of region of support for corner detection, Teh-Chin and Guru-Dinesh presented adaptive approaches based on some local properties of boundary points. However, these local-property based approaches are sensitive to noise. In this paper, we propose a new approach to find the optimum length of region of support for corner detection based on a statistic discriminant criterion. Since our approach is based on the global perspective of all boundary points, rather than the local properties of some points, the experiments show that the determined length of region of support increases as the noise intensity strengthens. In addition, the detected corners based on the optimum length of region of support are consistent with human experts' judgment, even for noisy boundaries.},
keywords={},
doi={10.1587/transinf.E92.D.2103},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - Optimizing Region of Support for Boundary-Based Corner Detection: A Statistic Approach
T2 - IEICE TRANSACTIONS on Information
SP - 2103
EP - 2111
AU - Wen-Bing HORNG
AU - Chun-Wen CHEN
PY - 2009
DO - 10.1587/transinf.E92.D.2103
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2009
AB - Boundary-based corner detection has been widely applied in spline curve fitting, automated optical inspection, image segmentation, object recognition, etc. In order to obtain good results, users usually need to adjust the length of region of support to resist zigzags due to quantization and random noise on digital boundaries. To automatically determine the length of region of support for corner detection, Teh-Chin and Guru-Dinesh presented adaptive approaches based on some local properties of boundary points. However, these local-property based approaches are sensitive to noise. In this paper, we propose a new approach to find the optimum length of region of support for corner detection based on a statistic discriminant criterion. Since our approach is based on the global perspective of all boundary points, rather than the local properties of some points, the experiments show that the determined length of region of support increases as the noise intensity strengthens. In addition, the detected corners based on the optimum length of region of support are consistent with human experts' judgment, even for noisy boundaries.
ER -