{"id":"https://openalex.org/W2542436715","doi":"https://doi.org/10.1109/iccv.2009.5459314","title":"Robust image segmentation using learned priors","display_name":"Robust image segmentation using learned priors","publication_year":2009,"publication_date":"2009-09-01","ids":{"openalex":"https://openalex.org/W2542436715","doi":"https://doi.org/10.1109/iccv.2009.5459314","mag":"2542436715"},"language":"en","primary_location":{"id":"doi:10.1109/iccv.2009.5459314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2009.5459314","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision","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/A5075057705","display_name":"Ayman El\u2010Baz","orcid":"https://orcid.org/0000-0001-7264-1323"},"institutions":[{"id":"https://openalex.org/I142740786","display_name":"University of Louisville","ror":"https://ror.org/01ckdn478","country_code":"US","type":"education","lineage":["https://openalex.org/I142740786"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ayman El-Baz","raw_affiliation_strings":["BioImaging Laboratory, University of Louisville, Louisville, KY, USA"],"affiliations":[{"raw_affiliation_string":"BioImaging Laboratory, University of Louisville, Louisville, KY, USA","institution_ids":["https://openalex.org/I142740786"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114377140","display_name":"Georgy Gimel\u2019farb","orcid":"https://orcid.org/0000-0003-2120-9391"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Georgy Gimel'farb","raw_affiliation_strings":["Computer Science Department, University of Auckland, Auckland, New Zealand"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075057705"],"corresponding_institution_ids":["https://openalex.org/I142740786"],"apc_list":null,"apc_paid":null,"fwci":1.6377,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.86907042,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"857","last_page":"864"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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":0.9980000257492065,"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/prior-probability","display_name":"Prior probability","score":0.8482228517532349},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.643964946269989},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5594759583473206},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.5367746353149414},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5355655550956726},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5090612769126892},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4983663558959961},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4772607982158661},{"id":"https://openalex.org/keywords/active-shape-model","display_name":"Active shape model","score":0.46426156163215637},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4481359124183655},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4478158950805664},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4238035976886749},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3701817989349365},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.23771139979362488},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15727713704109192},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1038648784160614}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.8482228517532349},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.643964946269989},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5594759583473206},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.5367746353149414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5355655550956726},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5090612769126892},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4983663558959961},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4772607982158661},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.46426156163215637},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4481359124183655},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4478158950805664},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4238035976886749},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3701817989349365},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.23771139979362488},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15727713704109192},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1038648784160614},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv.2009.5459314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2009.5459314","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision","raw_type":"proceedings-article"},{"id":"pmh:oai:researchspace.auckland.ac.nz:2292/17040","is_oa":false,"landing_page_url":"https://hdl.handle.net/2292/17040","pdf_url":null,"source":{"id":"https://openalex.org/S7407055463","display_name":"ResearchSpace (University of Auckland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I154130895","host_organization_name":"University of Auckland","host_organization_lineage":["https://openalex.org/I154130895"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Item"},{"id":"pmh:oai:researchspace.auckland.ac.nz:2292/18887","is_oa":false,"landing_page_url":"https://hdl.handle.net/2292/18887","pdf_url":null,"source":{"id":"https://openalex.org/S7407055463","display_name":"ResearchSpace (University of Auckland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I154130895","host_organization_name":"University of Auckland","host_organization_lineage":["https://openalex.org/I154130895"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Item"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1480873147","https://openalex.org/W1504719600","https://openalex.org/W1526745973","https://openalex.org/W1575139267","https://openalex.org/W1836111812","https://openalex.org/W1977946246","https://openalex.org/W1991113069","https://openalex.org/W2020790730","https://openalex.org/W2038952578","https://openalex.org/W2042695730","https://openalex.org/W2082541576","https://openalex.org/W2104095591","https://openalex.org/W2108077184","https://openalex.org/W2115021209","https://openalex.org/W2116040950","https://openalex.org/W2123491786","https://openalex.org/W2129116707","https://openalex.org/W2129212445","https://openalex.org/W2132396702","https://openalex.org/W2138524633","https://openalex.org/W2138905090","https://openalex.org/W2139559591","https://openalex.org/W2141587084","https://openalex.org/W2147484997","https://openalex.org/W2147594921","https://openalex.org/W2148107745","https://openalex.org/W2151103935","https://openalex.org/W2152826766","https://openalex.org/W2155617561","https://openalex.org/W2161557710","https://openalex.org/W2163551395","https://openalex.org/W2164867175","https://openalex.org/W2167338900","https://openalex.org/W2494521929","https://openalex.org/W2564629822","https://openalex.org/W3211330693","https://openalex.org/W6628806528","https://openalex.org/W6638680677","https://openalex.org/W6681134625","https://openalex.org/W6681371537"],"related_works":["https://openalex.org/W2381926679","https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W1990603712","https://openalex.org/W2250899144","https://openalex.org/W3016245616"],"abstract_inverted_index":{"A":[0],"novel":[1],"parametric":[2],"deformable":[3,92],"model":[4,93],"of":[5,31,33,61,70,135,164,177],"a":[6,28,49,104,146],"goal":[7,86],"object":[8,153],"controlled":[9],"by":[10,96],"shape":[11,23,109,141],"and":[12,73,110,113,126,142,145,154,175],"appearance":[13,44,111,116,143],"priors":[14,112,144],"learned":[15],"from":[16,39,79,88],"co-aligned":[17],"training":[18,37,66,81],"images":[19,171],"is":[20,25,46,94],"introduced.":[21],"The":[22,43,118,139],"prior":[24,45],"built":[26],"in":[27],"linear":[29,133],"space":[30],"vectors":[32],"distances":[34],"to":[35],"the":[36,71,80,91,108,114,152,165],"boundaries":[38],"their":[40],"common":[41],"centroid.":[42],"modeled":[47],"with":[48,103,131,169],"spatially":[50],"homogeneous":[51],"2":[52],"<sup":[53],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[54],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">nd</sup>":[55],"-order":[56],"Markov-Gibbs":[57],"random":[58],"field":[59],"(MGRF)":[60],"gray":[62,122],"levels":[63],"within":[64],"each":[65],"boundary.":[67],"Geometric":[68],"structure":[69],"MGRF":[72],"Gibbs":[74],"potentials":[75],"are":[76],"analytically":[77],"estimated":[78],"data.":[82],"To":[83],"accurately":[84],"separate":[85],"objects":[87],"arbitrary":[89],"background,":[90],"evolved":[95],"solving":[97],"an":[98,128],"Eikonal":[99],"partial":[100],"differential":[101],"equation":[102],"speed":[105,176],"function":[106],"combining":[107],"current":[115],"model.":[117],"latter":[119],"represents":[120],"empirical":[121],"level":[123],"marginals":[124],"inside":[125],"outside":[127],"evolving":[129],"boundary":[130],"adaptive":[132],"combinations":[134],"discrete":[136],"Gaussians":[137],"(LCDG).":[138],"analytical":[140],"simple":[147],"Expectation-Maximization":[148],"procedure":[149],"for":[150],"getting":[151],"background":[155],"LCDGs,":[156],"make":[157],"our":[158,178],"segmentation":[159],"considerably":[160],"faster":[161],"than":[162],"most":[163],"known":[166],"counterparts.":[167],"Experiments":[168],"various":[170],"confirm":[172],"robustness,":[173],"accuracy,":[174],"approach.":[179]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":2}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}