{"id":"https://openalex.org/W2099610552","doi":"https://doi.org/10.1109/cvpr.2008.4587450","title":"Shape prior segmentation of multiple objects with graph cuts","display_name":"Shape prior segmentation of multiple objects with graph cuts","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2099610552","doi":"https://doi.org/10.1109/cvpr.2008.4587450","mag":"2099610552"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2008.4587450","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587450","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-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/A5012650523","display_name":"Nhat Vu","orcid":"https://orcid.org/0009-0001-4661-4521"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nhat Vu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA","Dept. of Electr. & Comput. Eng., Univ. of California, Santa Barbara, CA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Univ. of California, Santa Barbara, CA#TAB#","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071938464","display_name":"B.S. Manjunath","orcid":"https://orcid.org/0000-0003-2804-3611"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"B.S. Manjunath","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA","Dept. of Electr. & Comput. Eng., Univ. of California, Santa Barbara, CA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Univ. of California, Santa Barbara, CA#TAB#","institution_ids":["https://openalex.org/I154570441"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":12.4251,"has_fulltext":false,"cited_by_count":157,"citation_normalized_percentile":{"value":0.98891533,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994000196456909,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9984999895095825,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9979000091552734,"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/cut","display_name":"Cut","score":0.7422769069671631},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6555716395378113},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6413306593894958},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.6234064102172852},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6052144765853882},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5994365811347961},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5421740412712097},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.5186406970024109},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5129641890525818},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.48170238733291626},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45483750104904175},{"id":"https://openalex.org/keywords/shape-analysis","display_name":"Shape analysis (program analysis)","score":0.45031359791755676},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44864943623542786},{"id":"https://openalex.org/keywords/active-shape-model","display_name":"Active shape model","score":0.41366660594940186},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2997271418571472},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.08468374609947205},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.08453130722045898}],"concepts":[{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.7422769069671631},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6555716395378113},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6413306593894958},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.6234064102172852},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6052144765853882},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5994365811347961},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5421740412712097},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.5186406970024109},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5129641890525818},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.48170238733291626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45483750104904175},{"id":"https://openalex.org/C112604564","wikidata":"https://www.wikidata.org/wiki/Q7489226","display_name":"Shape analysis (program analysis)","level":3,"score":0.45031359791755676},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44864943623542786},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.41366660594940186},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2997271418571472},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.08468374609947205},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.08453130722045898},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C97686452","wikidata":"https://www.wikidata.org/wiki/Q7604153","display_name":"Static analysis","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2008.4587450","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587450","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.139.400","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://vision.ece.ucsb.edu/publications/Vu_CVPR2008.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.409.231","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.409.231","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mplab.ucsd.edu/wp-content/uploads/cvpr2008/conference/data/papers/110.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W79315950","https://openalex.org/W1480873147","https://openalex.org/W1492498087","https://openalex.org/W1964119857","https://openalex.org/W1992192543","https://openalex.org/W1998074270","https://openalex.org/W2085787886","https://openalex.org/W2101309634","https://openalex.org/W2104095591","https://openalex.org/W2105365768","https://openalex.org/W2105811459","https://openalex.org/W2106569125","https://openalex.org/W2113137767","https://openalex.org/W2116040950","https://openalex.org/W2119300483","https://openalex.org/W2126789858","https://openalex.org/W2129390138","https://openalex.org/W2142425892","https://openalex.org/W2143452246","https://openalex.org/W2143516773","https://openalex.org/W2147484997","https://openalex.org/W2149184914","https://openalex.org/W2152435850","https://openalex.org/W2165734775","https://openalex.org/W2167338900","https://openalex.org/W2169527406","https://openalex.org/W2169551590","https://openalex.org/W2173414649","https://openalex.org/W2296770417","https://openalex.org/W3211330693","https://openalex.org/W6628806528"],"related_works":["https://openalex.org/W2501097992","https://openalex.org/W2464585191","https://openalex.org/W1965145450","https://openalex.org/W1990603712","https://openalex.org/W2250899144","https://openalex.org/W13786964","https://openalex.org/W2212089300","https://openalex.org/W2126962853","https://openalex.org/W1834774708","https://openalex.org/W2144085839"],"abstract_inverted_index":{"We":[0,31,70],"present":[1,33],"a":[2,23,34,64,85,108],"new":[3],"shape":[4,17,24,74,80],"prior":[5,18,75],"segmentation":[6,94],"method":[7],"using":[8],"graph":[9,36],"cuts":[10,52],"capable":[11],"of":[12,88,136],"segmenting":[13],"multiple":[14,68,79],"objects.":[15,45],"The":[16,46],"energy":[19,76,95],"is":[20,91,96],"based":[21],"on":[22,114],"distance":[25],"popular":[26],"with":[27,123],"level":[28],"set":[29],"approaches.":[30],"also":[32],"multiphase":[35,47],"cut":[37],"framework":[38],"to":[39,66,77,101],"simultaneously":[40],"segment":[41],"multiple,":[42],"possibly":[43],"overlapping":[44],"formulation":[48],"differs":[49],"from":[50],"multiway":[51],"in":[53],"that":[54,92,118],"the":[55,73,93,137],"former":[56],"can":[57,106,121],"account":[58],"for":[59],"object":[60],"overlaps":[61],"by":[62],"allowing":[63],"pixel":[65],"have":[67],"labels.":[69],"then":[71],"extend":[72],"encompass":[78],"priors.":[81],"Unlike":[82],"variational":[83],"methods,":[84],"major":[86],"advantage":[87],"our":[89,119],"approach":[90],"minimized":[97],"directly":[98],"without":[99],"having":[100],"compute":[102],"its":[103],"gradient,":[104],"which":[105],"be":[107],"cumbersome":[109],"task":[110],"and":[111,126,133],"often":[112],"relies":[113],"approximations.":[115],"Experiments":[116],"demonstrate":[117],"algorithm":[120],"cope":[122],"image":[124],"noise":[125],"clutter,":[127],"as":[128,130],"well":[129],"partial":[131],"occlusions":[132],"affine":[134],"transformations":[135],"shape.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":14},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":22},{"year":2013,"cited_by_count":21},{"year":2012,"cited_by_count":19}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}