A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation

Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c -means (FCM) clustering has also been suc...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on fuzzy systems Ročník 16; číslo 5; s. 1351 - 1361
Hlavní autoři:	Chatzis, S.P., Varvarigou, T.A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.10.2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Clustering Context modeling Fuzzy Fuzzy clustering Fuzzy logic Fuzzy set theory Hidden Markov models Image color analysis Image edge detection Image restoration Image segmentation Magnetorheological fluids Markov processes Markov random fields Mathematical models mean-field approximation Parameter estimation Pixel Stochastic processes Studies
ISSN:	1063-6706, 1941-0034
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c -means (FCM) clustering has also been successfully applied in several image segmentation applications. In this paper, we combine the benefits of these two approaches, by proposing a novel treatment of HMRF models, formulated on the basis of a fuzzy clustering principle. We approach the HMRF model treatment problem as an FCM-type clustering problem, effected by introducing the explicit assumptions of the HMRF model into the fuzzy clustering procedure. Our approach utilizes a fuzzy objective function regularized by Kullback--Leibler divergence information, and is facilitated by application of a mean-field-like approximation of the MRF prior. We experimentally demonstrate the superiority of the proposed approach over competing methodologies, considering a series of synthetic and real-world image segmentation applications.
AbstractList	Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c -means (FCM) clustering has also been successfully applied in several image segmentation applications. In this paper, we combine the benefits of these two approaches, by proposing a novel treatment of HMRF models, formulated on the basis of a fuzzy clustering principle. We approach the HMRF model treatment problem as an FCM-type clustering problem, effected by introducing the explicit assumptions of the HMRF model into the fuzzy clustering procedure. Our approach utilizes a fuzzy objective function regularized by Kullback--Leibler divergence information, and is facilitated by application of a mean-field-like approximation of the MRF prior. We experimentally demonstrate the superiority of the proposed approach over competing methodologies, considering a series of synthetic and real-world image segmentation applications. Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for.
Author	Chatzis, S.P. Varvarigou, T.A.
Author_xml	– sequence: 1 givenname: S.P. surname: Chatzis fullname: Chatzis, S.P. organization: Nat. Tech. Univ. of Athens, Athens – sequence: 2 givenname: T.A. surname: Varvarigou fullname: Varvarigou, T.A. organization: Nat. Tech. Univ. of Athens, Athens
BookMark	eNp9kU9P3DAQxaMKpALlC7QXq4f2FDr-m-S4WrEFCVQJlguXyBtPFlPH3tpJK_bT42VRDxy4eCz7995o5h0XBz54LIrPFM4ohebHcnF3f3_GAOrdIXP9UBzRRtASgIuDfAfFS1WB-lgcp_QIQIWk9VGxnZHFtN0-kbmb0ojR-jWZbTYx6O6BLMM_HQ25sMagJ9c6_g5_yY32JgxkYdEZch0MukT6EMm5f9C-Q0NuN3q02rnsGXwao7Y-v14Oeo3kFtcD-jEDwX8qDnvtEp6-1pNiuThfzi_Kq18_L-ezq7ITQMeyZxIYSiMbhoi9rjvg3KBi-UPJSlWryghqGqZ6bgBWlaSrlRQKK9oboPyk-L63zUP9mTCN7WBTh85pj2FKbV2DErXiVSa_vUtyIbNfIzL49Q34GKbo8xBtQxmHmjGeIbaHuhhSiti3m2gHHZ9aCu0utPYltHYXWvsaWhbVb0Sd3W9rt0f3vvTLXmrzmv73EgoqJil_BgsXp0k
CODEN	IEFSEV
CitedBy_id	crossref_primary_10_32604_cmc_2024_046094 crossref_primary_10_1007_s40815_017_0411_1 crossref_primary_10_1109_ACCESS_2020_3039742 crossref_primary_10_1109_TPAMI_2012_208 crossref_primary_10_1016_j_eswa_2024_124943 crossref_primary_10_1109_TFUZZ_2020_2966173 crossref_primary_10_1007_s11265_014_0898_8 crossref_primary_10_1109_TFUZZ_2019_2930030 crossref_primary_10_1016_j_patrec_2016_11_019 crossref_primary_10_1007_s10278_016_9934_5 crossref_primary_10_1109_JSTARS_2025_3542469 crossref_primary_10_1109_LGRS_2020_3010696 crossref_primary_10_1109_TGRS_2013_2287273 crossref_primary_10_3390_rs14143490 crossref_primary_10_3390_math9192383 crossref_primary_10_1109_ACCESS_2018_2889326 crossref_primary_10_1109_TGRS_2023_3328334 crossref_primary_10_1049_iet_ipr_2017_0166 crossref_primary_10_1016_j_eswa_2013_01_051 crossref_primary_10_1109_TGRS_2013_2281854 crossref_primary_10_1007_s11042_015_2795_y crossref_primary_10_1007_s10044_020_00897_2 crossref_primary_10_1007_s11042_022_11904_5 crossref_primary_10_1155_2019_5984649 crossref_primary_10_3390_rs15030828 crossref_primary_10_1155_2010_167942 crossref_primary_10_3390_rs12050783 crossref_primary_10_3390_rs11232772 crossref_primary_10_1016_j_eswa_2022_118811 crossref_primary_10_1016_j_neucom_2012_10_022 crossref_primary_10_1007_s11063_017_9672_9 crossref_primary_10_1016_j_patcog_2012_04_019 crossref_primary_10_1155_2015_240354 crossref_primary_10_1109_TFUZZ_2015_2505328 crossref_primary_10_3233_JIFS_211340 crossref_primary_10_1016_j_trc_2014_12_001 crossref_primary_10_1007_s10044_019_00826_y crossref_primary_10_1007_s12065_019_00266_x crossref_primary_10_1109_ACCESS_2019_2906332 crossref_primary_10_1109_TNNLS_2012_2228227 crossref_primary_10_1109_ACCESS_2022_3192967 crossref_primary_10_1109_TNN_2010_2054109 crossref_primary_10_1016_j_asoc_2019_105928 crossref_primary_10_1016_j_patcog_2012_11_026 crossref_primary_10_1109_ACCESS_2025_3540971 crossref_primary_10_3390_s18041299 crossref_primary_10_1016_j_asoc_2020_106318 crossref_primary_10_1109_TNNLS_2022_3190836 crossref_primary_10_1016_j_ijleo_2022_169039 crossref_primary_10_1109_TFUZZ_2023_3235392 crossref_primary_10_1109_TFUZZ_2013_2240689 crossref_primary_10_1109_TGRS_2018_2829630 crossref_primary_10_1049_iet_ipr_2016_0271 crossref_primary_10_3390_rs10050710 crossref_primary_10_1109_ACCESS_2018_2800058 crossref_primary_10_1016_j_patcog_2014_01_017 crossref_primary_10_3390_rs14153704 crossref_primary_10_1109_TIP_2015_2456505 crossref_primary_10_1016_j_engappai_2012_09_010 crossref_primary_10_1109_TGRS_2020_3035665 crossref_primary_10_1016_j_knosys_2021_108008 crossref_primary_10_1016_j_engappai_2022_104672 crossref_primary_10_1080_15732479_2015_1113300 crossref_primary_10_1049_iet_ipr_2012_0340 crossref_primary_10_1007_s40747_023_01129_w crossref_primary_10_1109_TMI_2011_2165342 crossref_primary_10_1016_j_patrec_2012_10_004 crossref_primary_10_1109_TITS_2018_2875159 crossref_primary_10_1109_TFUZZ_2013_2249072 crossref_primary_10_1145_3009967 crossref_primary_10_1007_s11042_023_16569_2 crossref_primary_10_1016_j_eswa_2012_05_097 crossref_primary_10_1109_TFUZZ_2018_2814591 crossref_primary_10_1016_j_optlastec_2013_10_003 crossref_primary_10_1049_iet_ipr_2017_0407 crossref_primary_10_3390_su151310374 crossref_primary_10_1016_j_imavis_2011_09_001 crossref_primary_10_1016_j_spasta_2019_03_002 crossref_primary_10_1109_TFUZZ_2015_2513091 crossref_primary_10_1016_j_cviu_2023_103765 crossref_primary_10_1088_1742_6596_1325_1_012064 crossref_primary_10_1155_2013_401413 crossref_primary_10_1109_TGRS_2021_3131272 crossref_primary_10_1111_rssc_12568 crossref_primary_10_1109_LGRS_2010_2040800 crossref_primary_10_1080_2150704X_2015_1093185 crossref_primary_10_1016_j_cag_2023_12_003 crossref_primary_10_1049_iet_ipr_2013_0178 crossref_primary_10_1109_LGRS_2015_2425225 crossref_primary_10_1016_j_media_2018_10_007 crossref_primary_10_3390_rs12071219 crossref_primary_10_1016_j_patcog_2020_107333 crossref_primary_10_1016_j_neucom_2015_10_087 crossref_primary_10_1016_j_protcy_2016_05_134 crossref_primary_10_1109_TMI_2017_2737232 crossref_primary_10_1016_j_dsp_2015_04_009 crossref_primary_10_1016_j_eswa_2016_07_039 crossref_primary_10_1016_j_asoc_2021_107245 crossref_primary_10_1016_j_rse_2017_10_001 crossref_primary_10_1080_01431161_2019_1629718 crossref_primary_10_1109_LSP_2012_2230626 crossref_primary_10_1109_TFUZZ_2014_2298244 crossref_primary_10_1109_TFUZZ_2018_2889018 crossref_primary_10_1007_s10044_017_0672_1 crossref_primary_10_1016_j_ins_2021_06_034 crossref_primary_10_1109_TFUZZ_2021_3099560 crossref_primary_10_1080_1206212X_2024_2380975 crossref_primary_10_3390_sym11060753 crossref_primary_10_1016_j_strusafe_2015_05_002 crossref_primary_10_1016_j_patcog_2011_10_010 crossref_primary_10_1080_24699322_2017_1389398 crossref_primary_10_1186_1687_5281_2013_63 crossref_primary_10_1007_s10916_016_0623_1 crossref_primary_10_1007_s12530_012_9066_1 crossref_primary_10_1016_j_ins_2015_10_031 crossref_primary_10_1109_TNN_2010_2046910 crossref_primary_10_1007_s13042_021_01429_y crossref_primary_10_3390_s17051114 crossref_primary_10_1109_TFUZZ_2018_2796074 crossref_primary_10_1016_j_psep_2019_01_005 crossref_primary_10_3390_app142311227 crossref_primary_10_3390_rs12244115 crossref_primary_10_1016_j_ins_2021_11_056 crossref_primary_10_3390_s19153285 crossref_primary_10_1109_JSTARS_2018_2875935 crossref_primary_10_1016_j_patcog_2016_06_020 crossref_primary_10_1016_j_ins_2018_08_015
Cites_doi	10.1109/TFUZZ.2004.841748 10.1109/TSMCB.2004.831165 10.1111/j.2517-6161.1986.tb01412.x 10.1109/83.210863 10.1016/S0165-0114(02)00372-X 10.1109/TFUZZ.2008.924317 10.1016/0031-3203(89)90011-3 10.1016/S0167-8655(98)00121-4 10.1109/TFUZZ.2006.889957 10.1109/91.873580 10.1109/TPAMI.2003.1227985 10.1016/S0019-9958(65)90241-X 10.1109/FUZZ.2001.1009107 10.1098/rsta.1991.0132 10.1109/TFUZZ.2007.902011 10.1109/3468.668967 10.1007/b106267 10.1007/978-1-4757-0450-1 10.1002/0471721182 10.1109/ICIP.2002.1039888 10.1109/TFUZZ.2004.840099 10.1109/ACVMOT.2005.71 10.1109/ICCV.2001.937655 10.1109/TIP.2007.891771 10.1109/TPAMI.1984.4767596 10.1016/0167-7152(90)90111-J 10.1109/CVPR.2005.390 10.1109/TGRS.1987.289751 10.1016/S0019-9958(69)90591-9 10.1016/S0031-3203(02)00027-4 10.2307/2987782 10.1109/TFUZZ.2006.876740 10.1109/42.996338 10.2307/2289127 10.1109/42.906424
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008
DBID	97E RIA RIE AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D 7SP F28 FR3
DOI	10.1109/TFUZZ.2008.2005008
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Electronics & Communications Abstracts ANTE: Abstracts in New Technology & Engineering Engineering Research Database
DatabaseTitle	CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional Electronics & Communications Abstracts Engineering Research Database ANTE: Abstracts in New Technology & Engineering
DatabaseTitleList	Technology Research Database Computer and Information Systems Abstracts Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1941-0034
EndPage	1361
ExternalDocumentID	2545341471 10_1109_TFUZZ_2008_2005008 4607251
Genre	orig-research
GroupedDBID	-~X .DC 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS TAE TN5 VH1 AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D RIG 7SP F28 FR3
ID	FETCH-LOGICAL-c401t-f2502e5d592eeefa8c033de62f2565767b7d41d926f3d00b751bb546e71fd013
IEDL.DBID	RIE
ISICitedReferencesCount	178
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000260046700019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1063-6706
IngestDate	Sun Sep 28 03:26:06 EDT 2025 Sat Sep 27 23:12:45 EDT 2025 Sun Jun 29 16:32:27 EDT 2025 Tue Nov 18 22:32:49 EST 2025 Sat Nov 29 06:46:21 EST 2025 Tue Aug 26 16:47:29 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	5
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c401t-f2502e5d592eeefa8c033de62f2565767b7d41d926f3d00b751bb546e71fd013
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
OpenAccessLink	http://doi.org/10.1109/TFUZZ.2008.2005008
PQID	912308223
PQPubID	23500
PageCount	11
ParticipantIDs	proquest_miscellaneous_880648637 proquest_miscellaneous_34501394 crossref_primary_10_1109_TFUZZ_2008_2005008 crossref_citationtrail_10_1109_TFUZZ_2008_2005008 ieee_primary_4607251 proquest_journals_912308223
PublicationCentury	2000
PublicationDate	2008-10-01
PublicationDateYYYYMMDD	2008-10-01
PublicationDate_xml	– month: 10 year: 2008 text: 2008-10-01 day: 01
PublicationDecade	2000
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on fuzzy systems
PublicationTitleAbbrev	TFUZZ
PublicationYear	2008
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref35 ref13 ref12 ref37 ref15 ref14 mclachlan (ref29) 2000 ref30 chandler (ref34) 1987 ref33 ref11 ref32 ref10 ref2 ref39 ref38 ref16 ref19 ref18 miyamoto (ref36) 1997; 2 geman (ref31) 1987 clifford (ref28) 1990 ref24 ref23 ref26 hjort (ref7) 1987; ge 25 ref25 ref20 ref42 ref41 ref22 ref21 ref43 winkler (ref1) 2006 ref27 dunmur (ref8) 2007 leski (ref17) 2001; 11 ref9 ref4 ref6 besag (ref3) 1986; 48 ref5 stanford (ref40) 1999
References_xml	– ident: ref26 doi: 10.1109/TFUZZ.2004.841748 – ident: ref13 doi: 10.1109/TSMCB.2004.831165 – volume: 48 start-page: 259 year: 1986 ident: ref3 article-title: on the statistical analysis of dirty pictures publication-title: J Roy Stat Soc B doi: 10.1111/j.2517-6161.1986.tb01412.x – ident: ref33 doi: 10.1109/83.210863 – ident: ref15 doi: 10.1016/S0165-0114(02)00372-X – year: 1999 ident: ref40 publication-title: Fast automatic unsupervised image segmentation and curve detection in spatial point patterns – ident: ref18 doi: 10.1109/TFUZZ.2008.924317 – ident: ref32 doi: 10.1016/0031-3203(89)90011-3 – ident: ref14 doi: 10.1016/S0167-8655(98)00121-4 – ident: ref21 doi: 10.1109/TFUZZ.2006.889957 – ident: ref16 doi: 10.1109/91.873580 – start-page: 371 year: 2007 ident: ref8 article-title: parameter estimation in latent profile models publication-title: Comput Stat Data Anal – year: 1987 ident: ref34 publication-title: Introduction to Modern Statistical Mechanics – year: 2006 ident: ref1 publication-title: Image Analysis Random Fields and Markov Chain Monte Carlo Methods – ident: ref35 doi: 10.1109/TPAMI.2003.1227985 – ident: ref10 doi: 10.1016/S0019-9958(65)90241-X – volume: 11 start-page: 993 year: 2001 ident: ref17 article-title: an -insensitive approach to fuzzy clustering publication-title: Int J Appl Math Comput Sci – year: 1990 ident: ref28 article-title: markov random fields in statistics publication-title: Disorder in Physical Systems A Volume in Honour of John M Hammersley on the Occasion of his 70th Birthday – ident: ref37 doi: 10.1109/FUZZ.2001.1009107 – ident: ref6 doi: 10.1098/rsta.1991.0132 – ident: ref22 doi: 10.1109/TFUZZ.2007.902011 – ident: ref24 doi: 10.1109/3468.668967 – ident: ref12 doi: 10.1007/b106267 – ident: ref11 doi: 10.1007/978-1-4757-0450-1 – year: 2000 ident: ref29 publication-title: Finite Mixture Models doi: 10.1002/0471721182 – ident: ref25 doi: 10.1109/ICIP.2002.1039888 – ident: ref19 doi: 10.1109/TFUZZ.2004.840099 – ident: ref43 doi: 10.1109/ACVMOT.2005.71 – ident: ref41 doi: 10.1109/ICCV.2001.937655 – ident: ref39 doi: 10.1109/TIP.2007.891771 – ident: ref4 doi: 10.1109/TPAMI.1984.4767596 – ident: ref2 doi: 10.1016/0167-7152(90)90111-J – ident: ref42 doi: 10.1109/CVPR.2005.390 – start-page: 1498 year: 1987 ident: ref31 publication-title: Proc Int Conf Math – volume: ge 25 start-page: 796 year: 1987 ident: ref7 article-title: a simulation study of some contextual classification methods for remotely sensed data publication-title: IEEE Trans Geosci Electron doi: 10.1109/TGRS.1987.289751 – ident: ref38 doi: 10.1016/S0019-9958(69)90591-9 – ident: ref9 doi: 10.1016/S0031-3203(02)00027-4 – ident: ref5 doi: 10.2307/2987782 – volume: 2 start-page: 86 year: 1997 ident: ref36 article-title: fuzzy -means as a regularization and maximum entropy approach publication-title: Proc 7th Int Fuzzy Syst Assoc World Congr – ident: ref20 doi: 10.1109/TFUZZ.2006.876740 – ident: ref23 doi: 10.1109/42.996338 – ident: ref27 doi: 10.2307/2289127 – ident: ref30 doi: 10.1109/42.906424
SSID	ssj0014518
Score	2.392393
Snippet	Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	1351
SubjectTerms	Clustering Context modeling Fuzzy Fuzzy clustering Fuzzy logic Fuzzy set theory Hidden Markov models Image color analysis Image edge detection Image restoration Image segmentation Magnetorheological fluids Markov processes Markov random fields Mathematical models mean-field approximation Parameter estimation Pixel Stochastic processes Studies
Title	A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation
URI	https://ieeexplore.ieee.org/document/4607251 https://www.proquest.com/docview/912308223 https://www.proquest.com/docview/34501394 https://www.proquest.com/docview/880648637
Volume	16
WOSCitedRecordID	wos000260046700019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0034 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014518 issn: 1063-6706 databaseCode: RIE dateStart: 19930101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VigMcKLQgQnn4wA1CHTuxneOqalQuFYJFqnqJEtuBSrsJ6m4qsb-eGeehSjwkbpEfiaPP9szYM_MBvK0EyqHEmzhzWYMGilNxraSKXU3EsDhjjLeBbEJfXJjLy_zTHryfY2G898H5zH-gx3CX7zrb01HZSaq4FhQvfU9rPcRqzTcGaZYMYW9KxkpzNQXI8PxkWXy9uhrcJukMhROV5B0hFFhVftuKg3wpDv5vZI_h0ahHssUA_BPY8-0hHEwcDWxcsofw8E7CwSPYLVjR73Y_2emqpwwJWMgWY1ZxtgwetOyckoq0jIJ4ulv2uWpdt2YFOboxIk5bbRjqueys_R58BxhxGuMcXuE7SdUkxgks_bjGfYp98d_WY2xT-xSWxdny9Dwe2RdiizbXNm5QORIeIcwF_m5TGculdF4JrFBopehauzRxuVCNdJzXCHldZ6nyOmkcKpbPYL_tWv8cmDC-yhInKlmb1GprGmHqhmM_L3xueQTJhEZpx8zkNNxVGSwUnpcBwYExc0Qwgndznx9DXo5_tj4izOaWI1wRHE-gl-PS3ZQ5ynJKgy8jeDPX4pqji5Sq9V2_KWWakeacRsD-0gJ3RZUaJfWLP3_5GB6IKbFu8hL2tze9fwX37e32enPzOszsXzTi9tU
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB5VBQk4UGhBhAL1gRuEOo7jOMdV1WgrygpBkKpeoiR2AGk3qbqbSt1fz4zzUCUeErfIj8TRZ3tm7Jn5AN4WAuVQYLUfmahGA8Uov1Sh8k1JxLA4Y7StHNlEvFjoi4vk8w68n2JhrLXO-cx-oEd3l2_aqqOjsmOpeCwoXvpeJKUI-mit6c5ARkEf-KZCX8VcjSEyPDnO0m-Xl73jJJ2icCKTvCOGHK_Kb5uxkzDp3v-N7Qk8HjRJNuuhfwo7ttmHvZGlgQ2Ldh8e3Uk5eADbGUu77faWnSw7ypGAhWw25BVnmfOhZXNKK9IwCuNpb9iXojHtiqXk6saIOm25ZqjpstPmh_MeYMRqjLN4ie8kZZM4J7D0bIU7Fftqv6-G6KbmGWTpaXYy9wf-Bb9Cq2vj16geCYsgJgJ_ty50xcPQWCWwQqGdEpexkYFJhKpDw3mJoJdlJJWNg9qgavkcdpu2sS-ACW2LKDCiCEstq7jStdBlzbGfFTapuAfBiEZeDbnJabjL3NkoPMkdgj1n5oCgB--mPld9Zo5_tj4gzKaWA1weHI6g58PiXecJSnNKhB96cDTV4qqjq5SisW23zkMZke4sPWB_aYH7opJahfHLP3_5CB7Ms0_n-fnZ4uMhPBRjmt3gFexurjv7Gu5XN5uf6-s3bpb_AipP-hw
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Fuzzy+Clustering+Approach+Toward+Hidden+Markov+Random+Field+Models+for+Enhanced+Spatially+Constrained+Image+Segmentation&rft.jtitle=IEEE+transactions+on+fuzzy+systems&rft.au=Chatzis%2C+S+P&rft.au=Varvarigou%2C+T+A&rft.date=2008-10-01&rft.issn=1063-6706&rft.volume=16&rft.issue=5&rft_id=info:doi/10.1109%2FTFUZZ.2008.2005008&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1063-6706&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1063-6706&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1063-6706&client=summon