{"id":"https://openalex.org/W1996902942","doi":"https://doi.org/10.1109/iccvw.2009.5457687","title":"Markov Chain Monte Carlo shape sampling using level sets","display_name":"Markov Chain Monte Carlo shape sampling using level sets","publication_year":2009,"publication_date":"2009-09-01","ids":{"openalex":"https://openalex.org/W1996902942","doi":"https://doi.org/10.1109/iccvw.2009.5457687","mag":"1996902942"},"language":"en","primary_location":{"id":"doi:10.1109/iccvw.2009.5457687","doi":"https://doi.org/10.1109/iccvw.2009.5457687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2009.5457687","pdf_url":null,"source":{"id":"https://openalex.org/S4393918695","display_name":"IEEE International Conference on Computer Vision Workshops","issn_l":"2473-9936","issn":["2473-9936"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops","raw_type":"proceedings-article"},"volume":null,"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/A5100460978","display_name":"Siqi Chen","orcid":"https://orcid.org/0000-0002-4156-5361","observed_orcids":["https://orcid.org/0000-0002-4156-5361"]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siqi Chen","raw_affiliation_strings":["Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA","Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy NY 12180, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy NY 12180, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049207791","display_name":"Richard J. Radke","orcid":"https://orcid.org/0000-0001-5064-7775","observed_orcids":["https://orcid.org/0000-0001-5064-7775"]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard J. Radke","raw_affiliation_strings":["Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA","Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy NY 12180, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy NY 12180, USA","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":3.0868,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.91556291,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"18","issue":null,"first_page":"296","last_page":"303"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.0026000000070780516,"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/T11830","display_name":"Point processes and geometric inequalities","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/level-sets","display_name":"level sets","score":0.6389999985694885},{"id":"https://openalex.org/keywords/signed-distance-function","display_name":"signed distance function","score":0.8619999885559082},{"id":"https://openalex.org/keywords/metropolis-hastings-algorithm","display_name":"Metropolis-Hastings algorithm","score":0.9100000262260437},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8899999856948853},{"id":"https://openalex.org/keywords/detailed-balance","display_name":"detailed balance","score":0.6669999957084656},{"id":"https://openalex.org/keywords/ergodicity","display_name":"ergodicity","score":0.7279999852180481},{"id":"https://openalex.org/keywords/implicit-shape-representation","display_name":"implicit shape representation","score":0.8560000061988831},{"id":"https://openalex.org/keywords/multimodal-distributions","display_name":"multimodal distributions","score":0.9309999942779541},{"id":"https://openalex.org/keywords/medical-image-segmentation","display_name":"medical image segmentation","score":0.9940000176429749}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6846648454666138},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5949243307113647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5937944650650024},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.5634715557098389},{"id":"https://openalex.org/C122044880","wikidata":"https://www.wikidata.org/wiki/Q5498822","display_name":"Ergodic theory","level":2,"score":0.5444296002388},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.5133034586906433},{"id":"https://openalex.org/C71169176","wikidata":"https://www.wikidata.org/wiki/Q7512907","display_name":"Signed distance function","level":2,"score":0.5080478191375732},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4768318235874176},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4687868356704712},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4554746150970459},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.45473557710647583},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4176537096500397},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4168652594089508},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37494248151779175},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3629160523414612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3185909390449524},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2738986015319824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18625369668006897},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1446179747581482},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1445014476776123},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.09168502688407898},{"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":2,"locations":[{"id":"doi:10.1109/iccvw.2009.5457687","doi":"https://doi.org/10.1109/iccvw.2009.5457687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2009.5457687","pdf_url":null,"source":{"id":"https://openalex.org/S4393918695","display_name":"IEEE International Conference on Computer Vision Workshops","issn_l":"2473-9936","issn":["2473-9936"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.164.6496","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.164.6496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ecse.rpi.edu/~rjradke/papers/chen-nordia09.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1480873147","https://openalex.org/W1486647633","https://openalex.org/W1555683961","https://openalex.org/W1598503809","https://openalex.org/W1601795611","https://openalex.org/W1604679511","https://openalex.org/W1663973292","https://openalex.org/W1991113069","https://openalex.org/W1992192543","https://openalex.org/W2008703230","https://openalex.org/W2020999234","https://openalex.org/W2021513670","https://openalex.org/W2030911724","https://openalex.org/W2042204212","https://openalex.org/W2048828405","https://openalex.org/W2056760934","https://openalex.org/W2074841047","https://openalex.org/W2093834886","https://openalex.org/W2098152234","https://openalex.org/W2101560656","https://openalex.org/W2102657595","https://openalex.org/W2104095591","https://openalex.org/W2106110775","https://openalex.org/W2106706098","https://openalex.org/W2110281611","https://openalex.org/W2129143463","https://openalex.org/W2130416410","https://openalex.org/W2132363464","https://openalex.org/W2138309709","https://openalex.org/W2147594921","https://openalex.org/W2153504150","https://openalex.org/W2155343350","https://openalex.org/W2159539239","https://openalex.org/W2167338900","https://openalex.org/W2168840843","https://openalex.org/W2535901150","https://openalex.org/W2998023265","https://openalex.org/W6629510986","https://openalex.org/W7046249567"],"related_works":["https://openalex.org/W2028024966","https://openalex.org/W2159539239","https://openalex.org/W2077837045","https://openalex.org/W2168840843","https://openalex.org/W7097791307","https://openalex.org/W2951977119","https://openalex.org/W2753935074","https://openalex.org/W2182075968","https://openalex.org/W78343534","https://openalex.org/W2978863544"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"show":[4,55,109],"how":[5],"the":[6,20,28,45,51,64,78,84,113],"Metropolis-Hastings":[7,32],"algorithm":[8],"can":[9,42,100],"be":[10,101],"used":[11],"to":[12,34,88,104,115],"sample":[13],"shapes":[14],"from":[15,118],"a":[16,38],"distribution":[17,40],"defined":[18],"over":[19,103],"space":[21],"of":[22,73,112],"signed":[23,46],"distance":[24,47],"functions.":[25],"We":[26,54,108],"extend":[27],"basic":[29],"random":[30],"walk":[31],"method":[33],"high-dimensional":[35],"curves":[36],"using":[37],"proposal":[39],"that":[41,56,63,77],"simultaneously":[43],"maintain":[44],"function":[48],"property":[49],"and":[50,62,121],"ergodic":[52],"requirement.":[53],"detailed":[57],"balance":[58],"is":[59,76,81,95],"approximately":[60],"satisfied":[61],"Markov":[65],"chain":[66],"will":[67],"asymptotically":[68],"converge.":[69],"A":[70],"key":[71],"advantage":[72],"our":[74,98],"approach":[75],"shape":[79,116],"representation":[80],"implicit":[82],"throughout":[83],"process,":[85],"as":[86],"compared":[87],"existing":[89],"work":[90],"where":[91],"explicit":[92],"curve":[93],"parameterization":[94],"required.":[96],"Furthermore,":[97],"framework":[99,114],"carried":[102],"3D":[105],"situations":[106],"easily.":[107],"several":[110],"applications":[111],"sampling":[117],"multimodal":[119],"distributions":[120],"medical":[122],"image":[123],"segmentation.":[124]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}