A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm
Euler’s elastica-based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler’s elastica-based approach that can properly deal with the ra...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
SPRINGER
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/91437 |
| _version_ | 1848765520449372160 |
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| author | Tan, Lu Li, Ling Liu, Wan-Quan Sun, Jie Zhang, M. |
| author_facet | Tan, Lu Li, Ling Liu, Wan-Quan Sun, Jie Zhang, M. |
| author_sort | Tan, Lu |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Euler’s elastica-based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler’s elastica-based approach that can properly deal with the random noises to improve the segmentation performance for noisy images. The corresponding formulation of stochastic optimization is solved via the progressive hedging algorithm (PHA), and the description of each individual scenario is obtained by the alternating direction method of multipliers. Technically, all the sub-problems derived from the framework of PHA can be solved by using the curvature-weighted approach and the convex relaxation method. Then, an alternating optimization strategy is applied by using some powerful accelerating techniques including the fast Fourier transform and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which displayed significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithms. |
| first_indexed | 2025-11-14T11:36:33Z |
| format | Journal Article |
| id | curtin-20.500.11937-91437 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:36:33Z |
| publishDate | 2020 |
| publisher | SPRINGER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-914372023-04-20T06:08:47Z A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm Tan, Lu Li, Ling Liu, Wan-Quan Sun, Jie Zhang, M. Science & Technology Technology Physical Sciences Computer Science, Artificial Intelligence Computer Science, Software Engineering Mathematics, Applied Computer Science Mathematics Euler's elastic energy Stochastic noises Progressive hedging algorithm (PHA ) Alternating direction method of multipliers (ADMM) Curvature-weighted approach ACTIVE CONTOURS FRAMEWORK Euler’s elastica-based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler’s elastica-based approach that can properly deal with the random noises to improve the segmentation performance for noisy images. The corresponding formulation of stochastic optimization is solved via the progressive hedging algorithm (PHA), and the description of each individual scenario is obtained by the alternating direction method of multipliers. Technically, all the sub-problems derived from the framework of PHA can be solved by using the curvature-weighted approach and the convex relaxation method. Then, an alternating optimization strategy is applied by using some powerful accelerating techniques including the fast Fourier transform and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which displayed significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithms. 2020 Journal Article http://hdl.handle.net/20.500.11937/91437 10.1007/s10851-019-00920-0 English SPRINGER fulltext |
| spellingShingle | Science & Technology Technology Physical Sciences Computer Science, Artificial Intelligence Computer Science, Software Engineering Mathematics, Applied Computer Science Mathematics Euler's elastic energy Stochastic noises Progressive hedging algorithm (PHA ) Alternating direction method of multipliers (ADMM) Curvature-weighted approach ACTIVE CONTOURS FRAMEWORK Tan, Lu Li, Ling Liu, Wan-Quan Sun, Jie Zhang, M. A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm |
| title | A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm |
| title_full | A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm |
| title_fullStr | A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm |
| title_full_unstemmed | A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm |
| title_short | A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm |
| title_sort | novel euler’s elastica-based segmentation approach for noisy images using the progressive hedging algorithm |
| topic | Science & Technology Technology Physical Sciences Computer Science, Artificial Intelligence Computer Science, Software Engineering Mathematics, Applied Computer Science Mathematics Euler's elastic energy Stochastic noises Progressive hedging algorithm (PHA ) Alternating direction method of multipliers (ADMM) Curvature-weighted approach ACTIVE CONTOURS FRAMEWORK |
| url | http://hdl.handle.net/20.500.11937/91437 |