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Multiclass segmentation by iterated ROF thresholding

Multiclass segmentation by iterated ROF thresholding
Multiclass segmentation by iterated ROF thresholding
Variational models as the Mumford-Shah model and the active contour model have many applications in image segmentation. In this paper, we propose a new multiclass segmentation model by combining the Rudin-Osher-Fatemi model with an iterative thresholding procedure. We show that our new model for two classes is indeed equivalent to the Chan-Vese model but with an adapted regularization parameter which allows to segment classes with similar gray values. We propose an efficient algorithm and discuss its convergence under certain conditions. Experiments on cartoon, texture and medical images demonstrate that our algorithm is not only fast but provides very good segmentation results in comparison with other state-of-the-art segmentation models in particular for images containing classes of similar gray values. © 2013 Springer-Verlag.
237-250
SpringerLink
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Steidl, Gabriele
c61576e3-9691-455d-bf74-7014841fb0de
Heyden, Anders
Kahl, Fredrik
Olsson, Carl
Oskarsson, Magnus
Tai, Xue-Cheng
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Steidl, Gabriele
c61576e3-9691-455d-bf74-7014841fb0de
Heyden, Anders
Kahl, Fredrik
Olsson, Carl
Oskarsson, Magnus
Tai, Xue-Cheng

Cai, Xiaohao and Steidl, Gabriele (2013) Multiclass segmentation by iterated ROF thresholding. In, Heyden, Anders, Kahl, Fredrik, Olsson, Carl, Oskarsson, Magnus and Tai, Xue-Cheng (eds.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), , (doi:10.1007/978-3-642-40395-8_18), 8081) Energy Minimization Methods in Computer Vision and Pattern Recognition: 9th International Conference (19/08/13 - 21/08/13) SpringerLink, pp. 237-250. (doi:10.1007/978-3-642-40395-8_18).

Record type: Book Section

Abstract

Variational models as the Mumford-Shah model and the active contour model have many applications in image segmentation. In this paper, we propose a new multiclass segmentation model by combining the Rudin-Osher-Fatemi model with an iterative thresholding procedure. We show that our new model for two classes is indeed equivalent to the Chan-Vese model but with an adapted regularization parameter which allows to segment classes with similar gray values. We propose an efficient algorithm and discuss its convergence under certain conditions. Experiments on cartoon, texture and medical images demonstrate that our algorithm is not only fast but provides very good segmentation results in comparison with other state-of-the-art segmentation models in particular for images containing classes of similar gray values. © 2013 Springer-Verlag.

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Published date: 2013
Venue - Dates: Energy Minimization Methods in Computer Vision and Pattern Recognition: 9th International Conference, Sweden, 2013-08-19 - 2013-08-21

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Local EPrints ID: 438601
URI: http://eprints.soton.ac.uk/id/eprint/438601
PURE UUID: 0f859cb7-7ff0-489a-a14e-689dec10c9c1

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Date deposited: 18 Mar 2020 17:30
Last modified: 19 May 2020 17:03

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Contributors

Author: Xiaohao Cai
Author: Gabriele Steidl
Editor: Anders Heyden
Editor: Fredrik Kahl
Editor: Carl Olsson
Editor: Magnus Oskarsson
Editor: Xue-Cheng Tai

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