{"id":"https://openalex.org/W2028620833","doi":"https://doi.org/10.1117/1.2139967","title":"Unsupervised Bayesian wavelet domain segmentation using Potts-Markov random field modeling","display_name":"Unsupervised Bayesian wavelet domain segmentation using Potts-Markov random field modeling","publication_year":2005,"publication_date":"2005-10-01","ids":{"openalex":"https://openalex.org/W2028620833","doi":"https://doi.org/10.1117/1.2139967","mag":"2028620833"},"language":"en","primary_location":{"id":"doi:10.1117/1.2139967","is_oa":false,"landing_page_url":"https://doi.org/10.1117/1.2139967","pdf_url":null,"source":{"id":"https://openalex.org/S158511090","display_name":"Journal of Electronic Imaging","issn_l":"1017-9909","issn":["1017-9909","1560-229X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Electronic Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041990954","display_name":"Ali Mohammad\u2010Djafari","orcid":"https://orcid.org/0000-0003-0678-7759"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210097418","display_name":"Laboratoire des signaux et syst\u00e8mes","ror":"https://ror.org/00skw9v43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I277688954","https://openalex.org/I277688954","https://openalex.org/I4210097418","https://openalex.org/I4210107720"]},{"id":"https://openalex.org/I102475099","display_name":"Sup\u00e9lec","ror":"https://ror.org/00n7gwn90","country_code":"FR","type":"education","lineage":["https://openalex.org/I102475099"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Ali Mohammad-Djafari","raw_affiliation_strings":["Lab. des signaux et syst\u00e8mes (France)","Laboratoire des Signaux et Syst\u00e8mes, CNRS UMR-8506, Supelec, Plateau du Moulon, 91192 Gif sur Yvette Cedex, France"],"affiliations":[{"raw_affiliation_string":"Lab. des signaux et syst\u00e8mes (France)","institution_ids":["https://openalex.org/I4210097418"]},{"raw_affiliation_string":"Laboratoire des Signaux et Syst\u00e8mes, CNRS UMR-8506, Supelec, Plateau du Moulon, 91192 Gif sur Yvette Cedex, France","institution_ids":["https://openalex.org/I102475099","https://openalex.org/I1294671590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5041990954"],"corresponding_institution_ids":["https://openalex.org/I102475099","https://openalex.org/I1294671590","https://openalex.org/I4210097418"],"apc_list":null,"apc_paid":null,"fwci":0.3061,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.59779431,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"14","issue":"4","first_page":"043011","last_page":"043011"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.614377498626709},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6103917956352234},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6005924344062805},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5294767618179321},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5197712779045105},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.49545133113861084},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49163323640823364},{"id":"https://openalex.org/keywords/potts-model","display_name":"Potts model","score":0.47211897373199463},{"id":"https://openalex.org/keywords/cascade-algorithm","display_name":"Cascade algorithm","score":0.45646965503692627},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44138965010643005},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4342024028301239},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.39640653133392334},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.38129720091819763},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35478949546813965},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.33767327666282654},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3144223392009735},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.07295867800712585},{"id":"https://openalex.org/keywords/ising-model","display_name":"Ising model","score":0.06216728687286377}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.614377498626709},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6103917956352234},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6005924344062805},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5294767618179321},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5197712779045105},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.49545133113861084},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49163323640823364},{"id":"https://openalex.org/C98925819","wikidata":"https://www.wikidata.org/wiki/Q7235385","display_name":"Potts model","level":3,"score":0.47211897373199463},{"id":"https://openalex.org/C88829872","wikidata":"https://www.wikidata.org/wiki/Q5048176","display_name":"Cascade algorithm","level":5,"score":0.45646965503692627},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44138965010643005},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4342024028301239},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.39640653133392334},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.38129720091819763},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35478949546813965},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.33767327666282654},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3144223392009735},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.07295867800712585},{"id":"https://openalex.org/C51329190","wikidata":"https://www.wikidata.org/wiki/Q1076349","display_name":"Ising model","level":2,"score":0.06216728687286377},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/1.2139967","is_oa":false,"landing_page_url":"https://doi.org/10.1117/1.2139967","pdf_url":null,"source":{"id":"https://openalex.org/S158511090","display_name":"Journal of Electronic Imaging","issn_l":"1017-9909","issn":["1017-9909","1560-229X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Electronic Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W5948861","https://openalex.org/W109881820","https://openalex.org/W1481864510","https://openalex.org/W1542333606","https://openalex.org/W1548481688","https://openalex.org/W1675664673","https://openalex.org/W1965511886","https://openalex.org/W1971784203","https://openalex.org/W1991806859","https://openalex.org/W2013848746","https://openalex.org/W2020999234","https://openalex.org/W2038948755","https://openalex.org/W2101778102","https://openalex.org/W2101897555","https://openalex.org/W2111727749","https://openalex.org/W2113945798","https://openalex.org/W2129482314","https://openalex.org/W2133157898","https://openalex.org/W2134929491","https://openalex.org/W2149375993","https://openalex.org/W2151658918","https://openalex.org/W2151890896","https://openalex.org/W2168796889","https://openalex.org/W2169280406","https://openalex.org/W2591023681"],"related_works":["https://openalex.org/W2144834862","https://openalex.org/W4384930455","https://openalex.org/W2385024119","https://openalex.org/W2394476054","https://openalex.org/W2394102384","https://openalex.org/W4391350003","https://openalex.org/W1974952288","https://openalex.org/W2386312039","https://openalex.org/W2358271565","https://openalex.org/W4391610546"],"abstract_inverted_index":{"We":[0],"describe":[1],"a":[2,11,15,34,50,62,96,105,114,121,130,151,181,214,218,234,261],"new":[3],"fully":[4,51],"unsupervised":[5,52],"image":[6,157,192,264],"segmentation":[7,30,279,291],"method":[8],"based":[9,32],"on":[10,33,54,271],"Bayesian":[12,29],"approach":[13,111],"and":[14,45,73,79,112,229,239,273,287],"Potts-Markov":[16],"random":[17],"field":[18],"(PMRF)":[19],"model":[20,48,65],"that":[21,277,289],"are":[22,198,256],"performed":[23],"in":[24,36,129,173,193,202,209,265],"the":[25,37,68,77,81,90,100,133,143,148,167,170,194,206,221,227,243,266,278,290],"wavelet":[26,171,195,207,253],"domain.":[27,268],"A":[28],"model,":[31],"PMRF":[35],"direct":[38,267],"domain,":[39,196,205],"has":[40],"already":[41],"been":[42],"successfully":[43],"developed":[44],"tested.":[46],"This":[47],"performs":[49],"segmentation,":[53,131],"images":[55,275],"composed":[56],"of":[57,89,120,124,135,150,154,166,169,183,237],"homogeneous":[58],"regions,":[59],"by":[60,104,180,187],"introducing":[61],"hidden":[63],"Markov":[64,106],"(HMM)":[66],"for":[67,76,80,99,147],"regions":[69],"to":[70,84,126,177,200,217,225,240,259],"be":[71,178,294],"classified,":[72],"Gaussian":[74],"distributions":[75,98],"noise":[78],"pixels":[82],"pertaining":[83],"each":[85],"region.":[86],"The":[87,118,251],"computation":[88],"posterior":[91],"laws,":[92],"deduced":[93],"from":[94],"these":[95],"priori":[97],"pixels,":[101],"is":[102,140,224],"done":[103],"chain":[107],"Monte":[108],"Carlo":[109],"(MCMC)":[110],"uses":[113],"Gibbs":[115],"sampling":[116],"algorithm.":[117],"use":[119],"high":[122,235],"number":[123,134,236],"iterations":[125],"reach":[127],"convergence":[128],"where":[132],"segments,":[136],"or":[137],"\"classes\"":[138],"labels,":[139],"important,":[141],"makes":[142],"algorithm":[144],"rather":[145],"slow":[146],"processing":[149],"large":[152],"quantity":[153],"data":[155],"like":[156],"sequences.":[158],"To":[159],"overcome":[160],"this":[161,203],"problem,":[162],"we":[163,197],"take":[164],"advantage":[165],"property":[168],"coefficients,":[172],"an":[174,189],"orthogonal":[175],"decomposition,":[176],"modeled":[179],"mixture":[182],"two":[184,211,249],"Gaussians.":[185],"Thus,":[186],"projecting":[188],"observable":[190],"noisy":[191,285],"able":[199],"segment,":[201],"same":[204],"subbands":[208,246],"only":[210,248],"classes.":[212,250],"After":[213],"decomposition":[215],"up":[216],"scale":[219],"J,":[220],"main":[222],"idea":[223],"segment":[226,241],"coarse,":[228],"small,":[230],"approximation":[231],"subband":[232],"with":[233,247,284],"classes,":[238],"all":[242],"detail":[244],"(wavelet)":[245],"segmented":[252,263],"domain":[254],"coefficients":[255],"then":[257],"reconstructed":[258],"obtain":[260],"final":[262],"Our":[269],"tests":[270],"synthetic":[272],"natural":[274],"show":[276],"quality":[280],"stays":[281],"good,":[282],"even":[283],"images,":[286],"shows":[288],"times":[292],"can":[293],"significantly":[295],"reduced.":[296]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}