{"id":"https://openalex.org/W2111755136","doi":"https://doi.org/10.1109/isspa.2010.5605506","title":"Hidden fuzzy Markov chain model with K discrete classes","display_name":"Hidden fuzzy Markov chain model with K discrete classes","publication_year":2010,"publication_date":"2010-05-01","ids":{"openalex":"https://openalex.org/W2111755136","doi":"https://doi.org/10.1109/isspa.2010.5605506","mag":"2111755136"},"language":"en","primary_location":{"id":"doi:10.1109/isspa.2010.5605506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isspa.2010.5605506","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010)","raw_type":"proceedings-article"},"type":"preprint","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/A5021360346","display_name":"Ahmed Gamal-Eldin","orcid":null},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Ahmed Gamal-Eldin","raw_affiliation_strings":["INRIA Sophia Antipolis 2004 - France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"INRIA Sophia Antipolis 2004 - France","institution_ids":["https://openalex.org/I1326498283"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059637695","display_name":"F. Salzenstein","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I68947357","display_name":"Universit\u00e9 de Strasbourg","ror":"https://ror.org/00pg6eq24","country_code":"FR","type":"education","lineage":["https://openalex.org/I68947357"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Fabien Salzenstein","raw_affiliation_strings":["Laboratoire InESS, UMR CNRS 7163 Université de Strasbourg - France","Laboratoire INESS, UMR CNRS 7163, Universit\u00e9 de Strasbourg, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratoire InESS, UMR CNRS 7163 Université de Strasbourg - France","institution_ids":["https://openalex.org/I68947357","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Laboratoire INESS, UMR CNRS 7163, Universit\u00e9 de Strasbourg, France","institution_ids":["https://openalex.org/I68947357","https://openalex.org/I1294671590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110302886","display_name":"Christophe Collet","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I68947357","display_name":"Universit\u00e9 de Strasbourg","ror":"https://ror.org/00pg6eq24","country_code":"FR","type":"education","lineage":["https://openalex.org/I68947357"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Christophe Collet","raw_affiliation_strings":["Université Strasbourg, LSIIT - UMR CNRS 7005 - France","Universit\u00e9 Strasbourg, LSIIT - UMR CNRS 7005 - France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Université Strasbourg, LSIIT - UMR CNRS 7005 - France","institution_ids":["https://openalex.org/I68947357","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Universit\u00e9 Strasbourg, LSIIT - UMR CNRS 7005 - France","institution_ids":["https://openalex.org/I68947357","https://openalex.org/I1294671590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3386,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60317662,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"109","last_page":"112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9868999719619751,"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"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9860000014305115,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7650954723358154},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.6150776743888855},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5798140168190002},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.5171867609024048},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5122656226158142},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5007941722869873},{"id":"https://openalex.org/keywords/bhattacharyya-distance","display_name":"Bhattacharyya distance","score":0.47575873136520386},{"id":"https://openalex.org/keywords/hidden-semi-markov-model","display_name":"Hidden semi-Markov model","score":0.46234598755836487},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45460647344589233},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44447222352027893},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.42283138632774353},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.412426620721817},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.40942463278770447},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.3533160090446472},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1910029649734497}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7650954723358154},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.6150776743888855},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5798140168190002},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.5171867609024048},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5122656226158142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5007941722869873},{"id":"https://openalex.org/C24145651","wikidata":"https://www.wikidata.org/wiki/Q2901249","display_name":"Bhattacharyya distance","level":2,"score":0.47575873136520386},{"id":"https://openalex.org/C64939953","wikidata":"https://www.wikidata.org/wiki/Q3859882","display_name":"Hidden semi-Markov model","level":5,"score":0.46234598755836487},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45460647344589233},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44447222352027893},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.42283138632774353},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.412426620721817},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.40942463278770447},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3533160090446472},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1910029649734497}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isspa.2010.5605506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isspa.2010.5605506","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.661.664","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.661.664","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://hal.archives-ouvertes.fr/hal-00616372/document/","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W435119396","https://openalex.org/W1974334754","https://openalex.org/W2019635781","https://openalex.org/W2044312370","https://openalex.org/W2049226298","https://openalex.org/W2086360377","https://openalex.org/W2112503046","https://openalex.org/W2124251574","https://openalex.org/W2124356058","https://openalex.org/W2133411727","https://openalex.org/W2912565176","https://openalex.org/W4211007335","https://openalex.org/W4249533638"],"related_works":["https://openalex.org/W2382132287","https://openalex.org/W2284415848","https://openalex.org/W1510894296","https://openalex.org/W2379938888","https://openalex.org/W3113307220","https://openalex.org/W4246505579","https://openalex.org/W2118728396","https://openalex.org/W2134386692","https://openalex.org/W2799426416","https://openalex.org/W2194396582"],"abstract_inverted_index":{"This":[0],"paper":[1],"deals":[2],"with":[3],"a":[4,32,46,55,62,66,87],"new":[5,33],"unsupervised":[6],"fuzzy":[7],"Bayesian":[8],"segmentation":[9,99],"method":[10],"based":[11,44],"on":[12,45],"the":[13,27,50,58,79],"hidden":[14,28,36,59],"Markov":[15,37],"chain":[16],"model,":[17],"in":[18,26,78],"order":[19],"to":[20,40,65,102],"separate":[21],"continuous":[22],"from":[23],"discrete":[24],"components":[25],"data.":[29,105],"We":[30,73,93],"present":[31,94],"F-HMC":[34],"(fuzzy":[35],"chain)":[38],"related":[39,101],"three":[41],"hard":[42],"classes,":[43],"general":[47],"extension":[48],"of":[49],"previously":[51],"algorithms":[52],"proposed.":[53],"For":[54],"given":[56],"observation,":[57],"variable":[60],"owns":[61],"density":[63],"according":[64],"measure":[67],"containing":[68],"Dirac":[69],"and":[70,97],"Lebesgue":[71],"components.":[72],"have":[74],"performed":[75],"our":[76],"approach":[77],"multispectral":[80],"context.":[81],"The":[82],"hyper-parameters":[83],"are":[84],"estimated":[85],"using":[86],"Stochastic":[88],"Expectation":[89],"Maximization":[90],"(SEM)":[91],"algorithm.":[92],"synthetic":[95],"simulations":[96],"also":[98],"results":[100],"real":[103],"multi-band":[104]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}