Edge detection models

Q. H. Zhang; S. Gao; Tien D. Bui

doi:10.1007/11559573_17

Edge detection models

Q. H. Zhang, S. Gao, Tien D. Bui

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

In this paper, the Mumford-Shah (MS) model and its variations are studied for image segmentation. It is found that using the piecewise constant approximation, we cannot detect edges with low contrast. Therefore other terms, such as gradient and Laplacian, are included in the models. To simplify the problem, the gradient of the original image is used in the Rudin-Osher-Fatemi (ROF) like model. It is found that this approximation is better than the piecewise constant approximation for some images since it can detect the low contrast edges of objects. Linear approximation is also used for both MS and ROF like models. It, is found that the linear approximation results are comparable with the results of the models using gradient and Laplacian terms.

Original language	English (US)
Title of host publication	Image Analysis and Recognition - Second International Conference, ICIAR 2005, Proceedings
Publisher	Springer Verlag
Pages	133-140
Number of pages	8
ISBN (Print)	3540290699, 9783540290698
DOIs	https://doi.org/10.1007/11559573_17
State	Published - 2005
Externally published	Yes
Event	2nd International Conference on Image Analysis and Recognition, ICIAR 2005 - Toronto, Canada Duration: Sep 28 2005 → Sep 30 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3656 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2nd International Conference on Image Analysis and Recognition, ICIAR 2005
Country/Territory	Canada
City	Toronto
Period	9/28/05 → 9/30/05

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/11559573_17

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Zhang, Q. H., Gao, S., & Bui, T. D. (2005). Edge detection models. In Image Analysis and Recognition - Second International Conference, ICIAR 2005, Proceedings (pp. 133-140). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3656 LNCS). Springer Verlag. https://doi.org/10.1007/11559573_17

Edge detection models. / Zhang, Q. H.; Gao, S.; Bui, Tien D.
Image Analysis and Recognition - Second International Conference, ICIAR 2005, Proceedings. Springer Verlag, 2005. p. 133-140 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3656 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, QH, Gao, S & Bui, TD 2005, Edge detection models. in Image Analysis and Recognition - Second International Conference, ICIAR 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3656 LNCS, Springer Verlag, pp. 133-140, 2nd International Conference on Image Analysis and Recognition, ICIAR 2005, Toronto, Canada, 9/28/05. https://doi.org/10.1007/11559573_17

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AB - In this paper, the Mumford-Shah (MS) model and its variations are studied for image segmentation. It is found that using the piecewise constant approximation, we cannot detect edges with low contrast. Therefore other terms, such as gradient and Laplacian, are included in the models. To simplify the problem, the gradient of the original image is used in the Rudin-Osher-Fatemi (ROF) like model. It is found that this approximation is better than the piecewise constant approximation for some images since it can detect the low contrast edges of objects. Linear approximation is also used for both MS and ROF like models. It, is found that the linear approximation results are comparable with the results of the models using gradient and Laplacian terms.

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Edge detection models

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