Voronoi tessellation and hierarchical model based texture image segmentation
A regional and statistical based algorithm for texture image segmentation was proposed. The Voronoi tessella-tion was used for partitioning the domain of an image into sub-regions corresponding to the components of homogenous regions, to which the texture image needs to be segmented. Bivariate Gauss...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2014-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.06.011/ |
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| _version_ | 1841539758654750720 |
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| author | Quan-hua ZHAO Yu LI Xiao-jun HE Wei-dong SONG |
| author_facet | Quan-hua ZHAO Yu LI Xiao-jun HE Wei-dong SONG |
| author_sort | Quan-hua ZHAO |
| collection | DOAJ |
| description | A regional and statistical based algorithm for texture image segmentation was proposed. The Voronoi tessella-tion was used for partitioning the domain of an image into sub-regions corresponding to the components of homogenous regions, to which the texture image needs to be segmented. Bivariate Gaussian Markov random field (BGMRF) model, static random field, and potts model were employed to characterize the interactions between two neighbor pixel pairs in a sub-region, and among sub-regions, respectively. Following Bayesian paradigm, a posterior distribution, which models the texture segmentation for a given texture image, was obtained. A metropolis-hastings algorithm was designed for simulating the posterior distribution. Then, texture segmentation was obtained by maximum a posterior (MAP) scheme. The proposed algorithm was tested with both of synthesized and real texture images. The results are qualitatively and quantitatively evaluated and show that the proposed algorithm works well on both of texture images. |
| format | Article |
| id | doaj-art-ae7b75316f02468c86db1ad73c49227b |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-ae7b75316f02468c86db1ad73c49227b2025-01-14T06:43:32ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-06-0135829159682091Voronoi tessellation and hierarchical model based texture image segmentationQuan-hua ZHAOYu LIXiao-jun HEWei-dong SONGA regional and statistical based algorithm for texture image segmentation was proposed. The Voronoi tessella-tion was used for partitioning the domain of an image into sub-regions corresponding to the components of homogenous regions, to which the texture image needs to be segmented. Bivariate Gaussian Markov random field (BGMRF) model, static random field, and potts model were employed to characterize the interactions between two neighbor pixel pairs in a sub-region, and among sub-regions, respectively. Following Bayesian paradigm, a posterior distribution, which models the texture segmentation for a given texture image, was obtained. A metropolis-hastings algorithm was designed for simulating the posterior distribution. Then, texture segmentation was obtained by maximum a posterior (MAP) scheme. The proposed algorithm was tested with both of synthesized and real texture images. The results are qualitatively and quantitatively evaluated and show that the proposed algorithm works well on both of texture images.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.06.011/texture segmentationVoronoi tessellation bivariate Gaussian Markov random field (BGMRF)Bayesian inference maximum a posterior (MAP) |
| spellingShingle | Quan-hua ZHAO Yu LI Xiao-jun HE Wei-dong SONG Voronoi tessellation and hierarchical model based texture image segmentation Tongxin xuebao texture segmentation Voronoi tessellation bivariate Gaussian Markov random field (BGMRF) Bayesian inference maximum a posterior (MAP) |
| title | Voronoi tessellation and hierarchical model based texture image segmentation |
| title_full | Voronoi tessellation and hierarchical model based texture image segmentation |
| title_fullStr | Voronoi tessellation and hierarchical model based texture image segmentation |
| title_full_unstemmed | Voronoi tessellation and hierarchical model based texture image segmentation |
| title_short | Voronoi tessellation and hierarchical model based texture image segmentation |
| title_sort | voronoi tessellation and hierarchical model based texture image segmentation |
| topic | texture segmentation Voronoi tessellation bivariate Gaussian Markov random field (BGMRF) Bayesian inference maximum a posterior (MAP) |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.06.011/ |
| work_keys_str_mv | AT quanhuazhao voronoitessellationandhierarchicalmodelbasedtextureimagesegmentation AT yuli voronoitessellationandhierarchicalmodelbasedtextureimagesegmentation AT xiaojunhe voronoitessellationandhierarchicalmodelbasedtextureimagesegmentation AT weidongsong voronoitessellationandhierarchicalmodelbasedtextureimagesegmentation |