{"id":"https://openalex.org/W2558047281","doi":"https://doi.org/10.1109/wcsp.2016.7752462","title":"A multiphase level set clustering approach using MRF-based Student's-t mixture model","display_name":"A multiphase level set clustering approach using MRF-based Student's-t mixture model","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2558047281","doi":"https://doi.org/10.1109/wcsp.2016.7752462","mag":"2558047281"},"language":"en","primary_location":{"id":"doi:10.1109/wcsp.2016.7752462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp.2016.7752462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th International Conference on Wireless Communications &amp; Signal Processing (WCSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5040434749","display_name":"Qunyi Xie","orcid":"https://orcid.org/0000-0002-5116-3526"},"institutions":[{"id":"https://openalex.org/I143593769","display_name":"East China University of Science and Technology","ror":"https://ror.org/01vyrm377","country_code":"CN","type":"education","lineage":["https://openalex.org/I143593769"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qunyi Xie","raw_affiliation_strings":["School of Information Science & Engineering, East China University of Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science & Engineering, East China University of Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I143593769"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100859124","display_name":"Xu Pan","orcid":null},"institutions":[{"id":"https://openalex.org/I143593769","display_name":"East China University of Science and Technology","ror":"https://ror.org/01vyrm377","country_code":"CN","type":"education","lineage":["https://openalex.org/I143593769"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Pan","raw_affiliation_strings":["School of Information Science & Engineering, East China University of Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science & Engineering, East China University of Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I143593769"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005274186","display_name":"Hongqing Zhu","orcid":"https://orcid.org/0000-0002-2122-7066"},"institutions":[{"id":"https://openalex.org/I143593769","display_name":"East China University of Science and Technology","ror":"https://ror.org/01vyrm377","country_code":"CN","type":"education","lineage":["https://openalex.org/I143593769"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongqing Zhu","raw_affiliation_strings":["School of Information Science & Engineering, East China University of Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science & Engineering, East China University of Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I143593769"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I143593769"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.98580002784729,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6747331619262695},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.576956570148468},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.4959495961666107},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.48756498098373413},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.46452581882476807},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4493284523487091},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.43522676825523376},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40018242597579956},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39893001317977905},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27348458766937256},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.24064978957176208},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23945701122283936},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09455415606498718}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6747331619262695},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.576956570148468},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.4959495961666107},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.48756498098373413},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.46452581882476807},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4493284523487091},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.43522676825523376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40018242597579956},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39893001317977905},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27348458766937256},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.24064978957176208},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23945701122283936},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09455415606498718},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcsp.2016.7752462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp.2016.7752462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th International Conference on Wireless Communications &amp; Signal Processing (WCSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8199999928474426,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1972168313","https://openalex.org/W2006012053","https://openalex.org/W2035531407","https://openalex.org/W2094952625","https://openalex.org/W2103099501","https://openalex.org/W2114487471","https://openalex.org/W2116040950","https://openalex.org/W2152595235","https://openalex.org/W2165734775","https://openalex.org/W2295021462","https://openalex.org/W3006564191","https://openalex.org/W4254011946","https://openalex.org/W6681655218"],"related_works":["https://openalex.org/W2473373438","https://openalex.org/W2955958993","https://openalex.org/W2368486525","https://openalex.org/W2077224612","https://openalex.org/W2153481672","https://openalex.org/W2153238387","https://openalex.org/W4312864369","https://openalex.org/W84255947","https://openalex.org/W2330365033","https://openalex.org/W2014842417"],"abstract_inverted_index":{"Student's-t":[0,27,104],"distribution":[1],"has":[2,159],"attracted":[3],"widely":[4],"attention":[5],"on":[6,171],"model-based":[7],"clustering":[8,33],"analysis.":[9],"In":[10],"this":[11],"paper,":[12],"we":[13],"propose":[14],"a":[15,43],"new":[16],"level":[17,54,74,137],"set":[18,55,75,138],"energy":[19],"function":[20,99],"framework":[21,127],"where":[22],"the":[23,48,61,69,72,80,85,92,96,101,112,125,135,152,156,165],"Markov":[24],"random":[25],"field-based":[26],"mixture":[28,105,121],"model":[29,122,148,158],"is":[30,64,89,110,118,128,144],"incorporated":[31],"for":[32],"both":[34],"static":[35],"images":[36],"and":[37,53,82,176,193],"time-series":[38,177],"data.":[39],"This":[40],"algorithm":[41,143],"provides":[42],"general":[44],"strategy":[45],"by":[46,77,150],"taking":[47],"best":[49],"of":[50,60,71,100,191],"Bayesian":[51],"technique":[52,94],"formulation.":[56],"A":[57],"remarkable":[58],"advantage":[59],"proposed":[62,93,126,157],"method":[63,76],"that":[65,111,124],"it":[66],"can":[67],"overcome":[68],"weakness":[70],"classical":[73],"filtering":[78],"out":[79],"outliers":[81],"stopping":[83],"at":[84],"boundary":[86],"points.":[87],"It":[88],"mainly":[90],"because":[91],"models":[95],"probability":[97],"density":[98],"data":[102,178],"via":[103],"model.":[106],"Another":[107],"attractive":[108],"feature":[109],"local":[113],"relationship":[114],"among":[115],"neighboring":[116],"pixels":[117],"considered":[119],"into":[120],"so":[123],"more":[129],"robust":[130],"against":[131],"noise":[132],"compared":[133,184],"to":[134,146,185],"other":[136,186],"based":[139],"models.":[140],"Expectation":[141],"maximization":[142],"applied":[145],"obtain":[147],"parameters":[149],"maximizing":[151],"log-likelihood":[153],"function.":[154],"Additionally,":[155],"simplified":[160],"structure":[161],"which":[162],"sharply":[163],"reduces":[164],"computational":[166],"complexity.":[167],"Finally,":[168],"numerical":[169],"experiments":[170],"various":[172],"synthetic,":[173],"real-world":[174],"images,":[175],"are":[179,183],"conducted.":[180],"The":[181],"performances":[182],"related":[187],"approaches":[188],"in":[189],"terms":[190],"effectiveness":[192],"accuracy.":[194]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
