Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and...
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Autor*in:	Guodong Wang [verfasserIn] Zhenkuan Pan [verfasserIn] Qian Dong [verfasserIn] Ximei Zhao [verfasserIn] Zhimei Zhang [verfasserIn] Jinming Duan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Übergeordnetes Werk:	In: Journal of Applied Mathematics - Hindawi Limited, 2006, (2014)
Übergeordnetes Werk:	year:2014

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1155/2014/614613

Katalog-ID:	DOAJ004228944

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allfields	10.1155/2014/614613 doi (DE-627)DOAJ004228944 (DE-599)DOAJfbf6f9f6016a4b9aa1f7d4a24020a71c DE-627 ger DE-627 rakwb eng QA1-939 Guodong Wang verfasserin aut Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images. Mathematics Zhenkuan Pan verfasserin aut Qian Dong verfasserin aut Ximei Zhao verfasserin aut Zhimei Zhang verfasserin aut Jinming Duan verfasserin aut In Journal of Applied Mathematics Hindawi Limited, 2006 (2014) (DE-627)638066859 (DE-600)2578385-3 16870042 nnns year:2014 https://doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/article/fbf6f9f6016a4b9aa1f7d4a24020a71c kostenfrei http://dx.doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/toc/1110-757X Journal toc kostenfrei https://doaj.org/toc/1687-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2014
spelling	10.1155/2014/614613 doi (DE-627)DOAJ004228944 (DE-599)DOAJfbf6f9f6016a4b9aa1f7d4a24020a71c DE-627 ger DE-627 rakwb eng QA1-939 Guodong Wang verfasserin aut Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images. Mathematics Zhenkuan Pan verfasserin aut Qian Dong verfasserin aut Ximei Zhao verfasserin aut Zhimei Zhang verfasserin aut Jinming Duan verfasserin aut In Journal of Applied Mathematics Hindawi Limited, 2006 (2014) (DE-627)638066859 (DE-600)2578385-3 16870042 nnns year:2014 https://doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/article/fbf6f9f6016a4b9aa1f7d4a24020a71c kostenfrei http://dx.doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/toc/1110-757X Journal toc kostenfrei https://doaj.org/toc/1687-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2014
allfields_unstemmed	10.1155/2014/614613 doi (DE-627)DOAJ004228944 (DE-599)DOAJfbf6f9f6016a4b9aa1f7d4a24020a71c DE-627 ger DE-627 rakwb eng QA1-939 Guodong Wang verfasserin aut Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images. Mathematics Zhenkuan Pan verfasserin aut Qian Dong verfasserin aut Ximei Zhao verfasserin aut Zhimei Zhang verfasserin aut Jinming Duan verfasserin aut In Journal of Applied Mathematics Hindawi Limited, 2006 (2014) (DE-627)638066859 (DE-600)2578385-3 16870042 nnns year:2014 https://doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/article/fbf6f9f6016a4b9aa1f7d4a24020a71c kostenfrei http://dx.doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/toc/1110-757X Journal toc kostenfrei https://doaj.org/toc/1687-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2014
allfieldsGer	10.1155/2014/614613 doi (DE-627)DOAJ004228944 (DE-599)DOAJfbf6f9f6016a4b9aa1f7d4a24020a71c DE-627 ger DE-627 rakwb eng QA1-939 Guodong Wang verfasserin aut Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images. Mathematics Zhenkuan Pan verfasserin aut Qian Dong verfasserin aut Ximei Zhao verfasserin aut Zhimei Zhang verfasserin aut Jinming Duan verfasserin aut In Journal of Applied Mathematics Hindawi Limited, 2006 (2014) (DE-627)638066859 (DE-600)2578385-3 16870042 nnns year:2014 https://doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/article/fbf6f9f6016a4b9aa1f7d4a24020a71c kostenfrei http://dx.doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/toc/1110-757X Journal toc kostenfrei https://doaj.org/toc/1687-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2014
allfieldsSound	10.1155/2014/614613 doi (DE-627)DOAJ004228944 (DE-599)DOAJfbf6f9f6016a4b9aa1f7d4a24020a71c DE-627 ger DE-627 rakwb eng QA1-939 Guodong Wang verfasserin aut Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images. Mathematics Zhenkuan Pan verfasserin aut Qian Dong verfasserin aut Ximei Zhao verfasserin aut Zhimei Zhang verfasserin aut Jinming Duan verfasserin aut In Journal of Applied Mathematics Hindawi Limited, 2006 (2014) (DE-627)638066859 (DE-600)2578385-3 16870042 nnns year:2014 https://doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/article/fbf6f9f6016a4b9aa1f7d4a24020a71c kostenfrei http://dx.doi.org/10.1155/2014/614613 kostenfrei https://doaj.org/toc/1110-757X Journal toc kostenfrei https://doaj.org/toc/1687-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2014
language	English
source	In Journal of Applied Mathematics (2014) year:2014
sourceStr	In Journal of Applied Mathematics (2014) year:2014
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Mathematics
isfreeaccess_bool	true
container_title	Journal of Applied Mathematics
authorswithroles_txt_mv	Guodong Wang @@aut@@ Zhenkuan Pan @@aut@@ Qian Dong @@aut@@ Ximei Zhao @@aut@@ Zhimei Zhang @@aut@@ Jinming Duan @@aut@@
publishDateDaySort_date	2014-01-01T00:00:00Z
hierarchy_top_id	638066859
id	DOAJ004228944
language_de	englisch
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author	Guodong Wang
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topic_title	QA1-939 Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information
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title	Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information
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title_full	Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information
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title_auth	Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information
abstract	Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.
abstractGer	Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.
abstract_unstemmed	Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.
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title_short	Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information
url	https://doi.org/10.1155/2014/614613 https://doaj.org/article/fbf6f9f6016a4b9aa1f7d4a24020a71c http://dx.doi.org/10.1155/2014/614613 https://doaj.org/toc/1110-757X https://doaj.org/toc/1687-0042
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up_date	2024-07-03T22:40:39.067Z
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