{"id":"https://openalex.org/W2579752474","doi":"https://doi.org/10.1109/ipta.2016.7821022","title":"MCMC based sampling technique for robust multi-model fitting and visual data segmentation","display_name":"MCMC based sampling technique for robust multi-model fitting and visual data segmentation","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2579752474","doi":"https://doi.org/10.1109/ipta.2016.7821022","mag":"2579752474"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2016.7821022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5075222297","display_name":"Alireza Sadri","orcid":"https://orcid.org/0000-0002-4196-4186"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Alireza Sadri","raw_affiliation_strings":["School of Engineering, RMIT University, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"School of Engineering, RMIT University, Melbourne, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022576557","display_name":"Ruwan Tennakoon","orcid":"https://orcid.org/0000-0001-8909-5728"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ruwan Tennakoon","raw_affiliation_strings":["School of Engineering, RMIT University, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"School of Engineering, RMIT University, Melbourne, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050176259","display_name":"Reza Hoseinnezhad","orcid":"https://orcid.org/0000-0001-9525-1467"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Reza Hoseinnezhad","raw_affiliation_strings":["School of Engineering, RMIT University, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"School of Engineering, RMIT University, Melbourne, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067249512","display_name":"Alireza Bab\u2010Hadiashar","orcid":"https://orcid.org/0000-0002-6192-2303"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Alireza Bab-Hadiashar","raw_affiliation_strings":["School of Engineering, RMIT University, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"School of Engineering, RMIT University, Melbourne, Australia","institution_ids":["https://openalex.org/I82951845"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.623,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":3,"citation_normalized_percentile":{"value":0.6175,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":77,"max":79},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9901,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9901,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9901,"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":0.9877,"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/image-segmentation","display_name":"Image Segmentation","score":0.541015},{"id":"https://openalex.org/keywords/statistical-shape-models","display_name":"Statistical Shape Models","score":0.513441},{"id":"https://openalex.org/keywords/mri-segmentation","display_name":"MRI Segmentation","score":0.512141}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7916262},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7272799},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6186306},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.61568034},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.58513796},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4731071},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.47150248},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.45487863},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.446338},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41633865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4132482},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.349891},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28249323},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18971598},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.15364301},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.11957973},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2016.7821022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":33,"referenced_works":["https://openalex.org/W1496571393","https://openalex.org/W1519594923","https://openalex.org/W152560764","https://openalex.org/W1550620085","https://openalex.org/W1575387439","https://openalex.org/W1910960015","https://openalex.org/W1927667002","https://openalex.org/W1993962865","https://openalex.org/W2013712253","https://openalex.org/W2022775854","https://openalex.org/W2033819227","https://openalex.org/W2037139490","https://openalex.org/W2052311585","https://openalex.org/W2053627315","https://openalex.org/W2076756823","https://openalex.org/W2099295295","https://openalex.org/W2103759851","https://openalex.org/W2104371080","https://openalex.org/W2106815954","https://openalex.org/W2108836590","https://openalex.org/W2117551572","https://openalex.org/W2135194391","https://openalex.org/W2135245997","https://openalex.org/W2143516773","https://openalex.org/W2149937146","https://openalex.org/W2158579916","https://openalex.org/W2164444636","https://openalex.org/W2165497092","https://openalex.org/W2165874743","https://openalex.org/W2202222344","https://openalex.org/W2294644361","https://openalex.org/W2498631646","https://openalex.org/W4250657332"],"related_works":["https://openalex.org/W4283726152","https://openalex.org/W3172507773","https://openalex.org/W3093571331","https://openalex.org/W3087071515","https://openalex.org/W2806680938","https://openalex.org/W2380816257","https://openalex.org/W2151689585","https://openalex.org/W1525770572","https://openalex.org/W1522196789","https://openalex.org/W1485888979"],"abstract_inverted_index":{"This":[0,116],"paper":[1],"approaches":[2],"the":[3,36,40,59,67,137],"problem":[4,13,38],"of":[5,20,49,98,142],"geometric":[6],"multi-model":[7],"fitting":[8],"as":[9],"a":[10,18,29,46,56,119],"data":[11],"segmentation":[12,37,146],"which":[14,123],"is":[15,45,63],"solved":[16],"by":[17,65],"sequence":[19],"sampling,":[21],"model":[22,125],"selection":[23],"and":[24,90,96,145],"clustering":[25,120],"steps.":[26],"We":[27],"propose":[28],"sampling":[30],"method":[31,52,133],"that":[32,62],"significantly":[33],"facilitates":[34],"solving":[35],"using":[39],"Normalized":[41],"cut.":[42],"The":[43,132],"sampler":[44],"novel":[47,88],"application":[48],"Markov-Chain-Monte-Carlo":[50],"(MCMC)":[51],"to":[53,93,108,118],"sample":[54,78],"from":[55,79],"distribution":[57,81],"in":[58,140],"parameter":[60],"space":[61],"obtained":[64],"modifying":[66],"Least":[68],"k":[69],"th":[72],"Order":[73],"Statistics":[74],"cost":[75],"function.":[76],"To":[77],"this":[80],"effectively,":[82],"our":[83],"proposed":[84],"Markov":[85],"Chain":[86],"includes":[87,103],"long":[89],"short":[91],"jumps":[92],"ensure":[94],"exploration":[95],"exploitation":[97],"all":[99],"structures.":[100,115],"It":[101],"also":[102],"fast":[104],"local":[105],"optimization":[106],"steps":[107],"target":[109],"all,":[110],"even":[111],"fairly":[112],"small,":[113],"putative":[114],"leads":[117],"solution":[121],"through":[122],"final":[124],"parameters":[126],"for":[127],"each":[128],"segment":[129],"are":[130],"obtained.":[131],"competes":[134],"favorably":[135],"with":[136],"state-of-the-art":[138],"both":[139],"terms":[141],"computation":[143],"power":[144],"accuracy.":[147]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2579752474","counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2024-12-05T05:59:42.570632","created_date":"2017-01-26"}