{"id":"https://openalex.org/W2015898655","doi":"https://doi.org/10.1109/cvpr.2015.7299103","title":"Situational object boundary detection","display_name":"Situational object boundary detection","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W2015898655","doi":"https://doi.org/10.1109/cvpr.2015.7299103","mag":"2015898655"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7299103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.research.ed.ac.uk/en/publications/7c66dcf7-67b1-443c-8eed-8d465bb24bcf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020938231","display_name":"Jasper Uijlings","orcid":"https://orcid.org/0000-0002-3288-7377"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"J.R.R. Uijlings","raw_affiliation_strings":["University of Edinburgh","University of Edinburgh. Scotland"],"affiliations":[{"raw_affiliation_string":"University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"University of Edinburgh. Scotland","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101423778","display_name":"Vittorio Ferrari","orcid":"https://orcid.org/0000-0002-1942-233X"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"V. Ferrari","raw_affiliation_strings":["University of Edinburgh","University of Edinburgh. Scotland"],"affiliations":[{"raw_affiliation_string":"University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"University of Edinburgh. Scotland","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020938231"],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":2.2091,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.9179927,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4712","last_page":"4721"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval 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/T10627","display_name":"Advanced Image and Video Retrieval 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":0.9991999864578247,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9991999864578247,"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/object-detection","display_name":"Object detection","score":0.6923871040344238},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.6891401410102844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6819121241569519},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6499603390693665},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.6495925188064575},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5441608428955078},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5391272902488708},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5205219984054565},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4640656113624573},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.46362829208374023},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43580538034439087},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4340501129627228},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42847418785095215},{"id":"https://openalex.org/keywords/viola\u2013jones-object-detection-framework","display_name":"Viola\u2013Jones object detection framework","score":0.42627543210983276},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.42459604144096375},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.41175174713134766},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18630346655845642},{"id":"https://openalex.org/keywords/face-detection","display_name":"Face detection","score":0.10861402750015259}],"concepts":[{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6923871040344238},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.6891401410102844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819121241569519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6499603390693665},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.6495925188064575},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5441608428955078},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5391272902488708},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5205219984054565},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4640656113624573},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.46362829208374023},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43580538034439087},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4340501129627228},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42847418785095215},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.42627543210983276},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.42459604144096375},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.41175174713134766},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18630346655845642},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.10861402750015259},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2015.7299103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/7c66dcf7-67b1-443c-8eed-8d465bb24bcf","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/7c66dcf7-67b1-443c-8eed-8d465bb24bcf","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Uijlings, J R R & Ferrari, V 2015, Situational Object Boundary Detection. in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Institute of Electrical and Electronics Engineers, pp. 4712-4721, 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, United States, 8/06/15. https://doi.org/10.1109/CVPR.2015.7299103","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.ed.ac.uk:publications/7c66dcf7-67b1-443c-8eed-8d465bb24bcf","is_oa":false,"landing_page_url":"https://www.research.ed.ac.uk/portal/en/publications/situational-object-boundary-detection(7c66dcf7-67b1-443c-8eed-8d465bb24bcf).html","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:openaire/7c66dcf7-67b1-443c-8eed-8d465bb24bcf","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/7c66dcf7-67b1-443c-8eed-8d465bb24bcf","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Uijlings, J R R & Ferrari, V 2015, Situational Object Boundary Detection. in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Institute of Electrical and Electronics Engineers, pp. 4712-4721, 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, United States, 8/06/15. https://doi.org/10.1109/CVPR.2015.7299103","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6000000238418579,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1528147907","https://openalex.org/W1567856076","https://openalex.org/W1606858007","https://openalex.org/W1625255723","https://openalex.org/W1861492603","https://openalex.org/W1864464506","https://openalex.org/W1994002998","https://openalex.org/W2003370853","https://openalex.org/W2013777358","https://openalex.org/W2037227137","https://openalex.org/W2062118960","https://openalex.org/W2066941820","https://openalex.org/W2102605133","https://openalex.org/W2108729336","https://openalex.org/W2110158442","https://openalex.org/W2117539524","https://openalex.org/W2117741877","https://openalex.org/W2118087936","https://openalex.org/W2119823327","https://openalex.org/W2120725344","https://openalex.org/W2121947440","https://openalex.org/W2125849446","https://openalex.org/W2129587342","https://openalex.org/W2131846894","https://openalex.org/W2144794286","https://openalex.org/W2145023731","https://openalex.org/W2151049637","https://openalex.org/W2151103935","https://openalex.org/W2151259137","https://openalex.org/W2155541015","https://openalex.org/W2155893237","https://openalex.org/W2162915993","https://openalex.org/W2163605009","https://openalex.org/W2165914352","https://openalex.org/W2166761907","https://openalex.org/W2168356304","https://openalex.org/W2185707253","https://openalex.org/W2534457893","https://openalex.org/W2911964244","https://openalex.org/W3017143921","https://openalex.org/W4251530686","https://openalex.org/W4294375521","https://openalex.org/W6636412649","https://openalex.org/W6636494156","https://openalex.org/W6639102338","https://openalex.org/W6639328394","https://openalex.org/W6659984577","https://openalex.org/W6676449312","https://openalex.org/W6677945368","https://openalex.org/W6678684981","https://openalex.org/W6679405645","https://openalex.org/W6682778277","https://openalex.org/W6684191040","https://openalex.org/W6997266731","https://openalex.org/W7075671638"],"related_works":["https://openalex.org/W3177249605","https://openalex.org/W2534152068","https://openalex.org/W4299545679","https://openalex.org/W1972515067","https://openalex.org/W1689909837","https://openalex.org/W4293054914","https://openalex.org/W2549121492","https://openalex.org/W3138508047","https://openalex.org/W3160205797","https://openalex.org/W4376620596"],"abstract_inverted_index":{"Intuitively,":[0],"the":[1,13,91,102],"appearance":[2],"of":[3,17,30,53,64],"true":[4],"object":[5,45,59,94,124],"boundaries":[6],"varies":[7],"from":[8],"image":[9,87],"to":[10,26,35],"image.":[11],"Hence":[12],"usual":[14],"monolithic":[15,132],"approach":[16],"training":[18],"a":[19,51,57,70,131],"single":[20],"boundary":[21,46,60,95,125],"predictor":[22],"and":[23,55,97,113],"applying":[24],"it":[25,75],"all":[27],"images":[28],"regardless":[29],"their":[31,145],"content":[32],"is":[33],"bound":[34],"be":[36],"suboptimal.":[37],"In":[38,105],"this":[39],"paper":[40],"we":[41,73,83,119],"therefore":[42],"propose":[43],"situational":[44,93,123],"detection:":[47],"We":[48,89],"first":[49],"define":[50],"variety":[52],"situations":[54,78],"train":[56],"specialized":[58],"detector":[61],"for":[62],"each":[63],"them":[65,99],"using":[66,79],"[10].":[67],"Then":[68],"given":[69],"test":[71],"image,":[72],"classify":[74],"into":[76],"these":[77],"its":[80],"context,":[81],"which":[82],"model":[84],"by":[85],"global":[86],"appearance.":[88],"apply":[90],"corresponding":[92],"detectors,":[96],"fuse":[98],"based":[100],"on":[101,107,140,144],"classification":[103],"probabilities.":[104],"experiments":[106],"ImageNet":[108],"[35],":[109],"Microsoft":[110],"COCO":[111],"[24],":[112],"Pascal":[114],"VOC":[115],"2012":[116],"segmentation":[117],"[13]":[118],"show":[120],"that":[121],"our":[122,135],"detection":[126,143],"gives":[127],"significant":[128],"improvements":[129],"over":[130],"approach.":[133],"Additionally,":[134],"method":[136],"substantially":[137],"outperforms":[138],"[17]":[139],"semantic":[141],"contour":[142],"SBD":[146],"dataset.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
