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- Publisher Website: 10.4208/cicp.160609.311209a
- Scopus: eid_2-s2.0-77952677675
- WOS: WOS:000281405000008
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Article: Kernel density estimation basedmultiphase fuzzy region competition method for texture image segmentation
Title | Kernel density estimation basedmultiphase fuzzy region competition method for texture image segmentation |
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Authors | |
Keywords | Fuzzy membership function Kernel density estimation Multiphase region competition Texture Total variation |
Issue Date | 2010 |
Citation | Communications in Computational Physics, 2010, v. 8, n. 3, p. 623-641 How to Cite? |
Abstract | In this paper, we propose a multiphase fuzzy region competition model for texture image segmentation. In the functional, each region is represented by a fuzzy membership function and a probability density function that is estimated by a nonparametric kernel density estimation. The overall algorithmis very efficient as both the fuzzy membership function and the probability density function can be implemented easily. We apply the proposed method to synthetic and natural texture images, and synthetic aperture radar images. Our experimental results have shown that the proposed method is competitive with the other state-of-the-art segmentation methods. © 2010 Global-Science Press. |
Persistent Identifier | http://hdl.handle.net/10722/276862 |
ISSN | 2023 Impact Factor: 2.6 2023 SCImago Journal Rankings: 1.176 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, Fang | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:34:52Z | - |
dc.date.available | 2019-09-18T08:34:52Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Communications in Computational Physics, 2010, v. 8, n. 3, p. 623-641 | - |
dc.identifier.issn | 1815-2406 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276862 | - |
dc.description.abstract | In this paper, we propose a multiphase fuzzy region competition model for texture image segmentation. In the functional, each region is represented by a fuzzy membership function and a probability density function that is estimated by a nonparametric kernel density estimation. The overall algorithmis very efficient as both the fuzzy membership function and the probability density function can be implemented easily. We apply the proposed method to synthetic and natural texture images, and synthetic aperture radar images. Our experimental results have shown that the proposed method is competitive with the other state-of-the-art segmentation methods. © 2010 Global-Science Press. | - |
dc.language | eng | - |
dc.relation.ispartof | Communications in Computational Physics | - |
dc.subject | Fuzzy membership function | - |
dc.subject | Kernel density estimation | - |
dc.subject | Multiphase region competition | - |
dc.subject | Texture | - |
dc.subject | Total variation | - |
dc.title | Kernel density estimation basedmultiphase fuzzy region competition method for texture image segmentation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.4208/cicp.160609.311209a | - |
dc.identifier.scopus | eid_2-s2.0-77952677675 | - |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 623 | - |
dc.identifier.epage | 641 | - |
dc.identifier.eissn | 1991-7120 | - |
dc.identifier.isi | WOS:000281405000008 | - |
dc.identifier.issnl | 1815-2406 | - |