{"id":"https://openalex.org/W3185139917","doi":"https://doi.org/10.1109/sais53221.2021.9484039","title":"Predicting Signed Distance Functions for Visual Instance Segmentation","display_name":"Predicting Signed Distance Functions for Visual Instance Segmentation","publication_year":2021,"publication_date":"2021-06-14","ids":{"openalex":"https://openalex.org/W3185139917","doi":"https://doi.org/10.1109/sais53221.2021.9484039","mag":"3185139917"},"language":"en","primary_location":{"id":"doi:10.1109/sais53221.2021.9484039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sais53221.2021.9484039","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Swedish Artificial Intelligence Society Workshop (SAIS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179288","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001393164","display_name":"Emil Brissman","orcid":"https://orcid.org/0000-0002-0418-9694"},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]},{"id":"https://openalex.org/I4210153572","display_name":"Saab (Sweden)","ror":"https://ror.org/05kyy7d06","country_code":"SE","type":"company","lineage":["https://openalex.org/I4210153572"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Emil Brissman","raw_affiliation_strings":["Dept. of Electrical Engineering, Computer Vision Laboratory, Link\u00f6ping University","Saab, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering, Computer Vision Laboratory, Link\u00f6ping University","institution_ids":["https://openalex.org/I102134673"]},{"raw_affiliation_string":"Saab, Sweden","institution_ids":["https://openalex.org/I4210153572"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037299085","display_name":"Joakim Johnander","orcid":"https://orcid.org/0000-0003-2553-3367"},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Joakim Johnander","raw_affiliation_strings":["Dept. of Electrical Engineering, Computer Vision Laboratory, Link\u00f6ping University","Zenseact, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering, Computer Vision Laboratory, Link\u00f6ping University","institution_ids":["https://openalex.org/I102134673"]},{"raw_affiliation_string":"Zenseact, Sweden","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042087981","display_name":"Michael Felsberg","orcid":"https://orcid.org/0000-0002-6096-3648"},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Michael Felsberg","raw_affiliation_strings":["Dept. of Electrical Engineering, Computer Vision Laboratory, Link\u00f6ping University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering, Computer Vision Laboratory, Link\u00f6ping University","institution_ids":["https://openalex.org/I102134673"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2911,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55437816,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9990000128746033,"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":0.9990000128746033,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9976000189781189,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9965000152587891,"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/segmentation","display_name":"Segmentation","score":0.7826498746871948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7324380278587341},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6491603851318359},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5869256854057312},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5749001502990723},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5550211668014526},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5537727475166321},{"id":"https://openalex.org/keywords/distance-transform","display_name":"Distance transform","score":0.5275083184242249},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5042449235916138},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.489275187253952},{"id":"https://openalex.org/keywords/rectangle","display_name":"Rectangle","score":0.4854055345058441},{"id":"https://openalex.org/keywords/signed-distance-function","display_name":"Signed distance function","score":0.4723045825958252},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4711267650127411},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.46940815448760986},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.46159908175468445},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.43805286288261414},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.43606171011924744},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4266689419746399},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4255021810531616},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28070515394210815},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.27605754137039185},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.10049659013748169},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07326862215995789}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7826498746871948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7324380278587341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6491603851318359},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5869256854057312},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5749001502990723},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5550211668014526},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5537727475166321},{"id":"https://openalex.org/C73621898","wikidata":"https://www.wikidata.org/wiki/Q2940504","display_name":"Distance transform","level":3,"score":0.5275083184242249},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5042449235916138},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.489275187253952},{"id":"https://openalex.org/C2781302577","wikidata":"https://www.wikidata.org/wiki/Q209","display_name":"Rectangle","level":2,"score":0.4854055345058441},{"id":"https://openalex.org/C71169176","wikidata":"https://www.wikidata.org/wiki/Q7512907","display_name":"Signed distance function","level":2,"score":0.4723045825958252},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4711267650127411},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.46940815448760986},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.46159908175468445},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.43805286288261414},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.43606171011924744},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4266689419746399},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4255021810531616},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28070515394210815},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27605754137039185},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.10049659013748169},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07326862215995789},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/sais53221.2021.9484039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sais53221.2021.9484039","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Swedish Artificial Intelligence Society Workshop (SAIS)","raw_type":"proceedings-article"},{"id":"pmh:oai:DiVA.org:liu-179288","is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179288","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","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":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:DiVA.org:liu-179288","is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179288","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322327","display_name":"Knut och Alice Wallenbergs Stiftelse","ror":"https://ror.org/004hzzk67"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2935837427","https://openalex.org/W2963150697","https://openalex.org/W2963627347","https://openalex.org/W2963775509","https://openalex.org/W2982161360","https://openalex.org/W2993182889","https://openalex.org/W3030520226","https://openalex.org/W3034259269","https://openalex.org/W3034549805","https://openalex.org/W3096609285","https://openalex.org/W3106250896","https://openalex.org/W6639102338","https://openalex.org/W6760424586","https://openalex.org/W6778485988","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2185902295","https://openalex.org/W2945274617","https://openalex.org/W2785294226","https://openalex.org/W2068271400","https://openalex.org/W2372421320","https://openalex.org/W2057775483","https://openalex.org/W2041871225","https://openalex.org/W2386644571","https://openalex.org/W2147425361"],"abstract_inverted_index":{"Visual":[0],"instance":[1,37,162,205,219],"segmentation":[2,154,163,171],"is":[3,30,105,196,213],"a":[4,26,58,86,93,120,148,173,214],"challenging":[5],"problem":[6],"and":[7,55,89],"becomes":[8],"even":[9],"more":[10],"difficult":[11],"if":[12],"objects":[13,21,40],"of":[14,61,81,178],"interest":[15],"varies":[16],"unconstrained":[17],"in":[18,119,144,176,229],"shape.":[19],"Some":[20],"are":[22],"well":[23,71],"described":[24],"by":[25],"rectangle,":[27],"however,":[28],"this":[29,153,211],"hardly":[31],"always":[32],"the":[33,112,115,125,133,160,167,186,192,203,225,230],"case.":[34],"Consider":[35],"for":[36,218],"long,":[38],"slender":[39],"such":[41],"as":[42,50,221],"ropes.":[43],"Anchor-based":[44],"approaches":[45],"classify":[46],"predefined":[47],"bounding":[48],"boxes":[49],"either":[51],"negative":[52],"or":[53],"positive":[54],"thus":[56],"provide":[57],"limited":[59],"set":[60],"shapes":[62,75,227],"that":[63,69,210],"can":[64],"be":[65,142],"handled.":[66],"Defining":[67],"anchor-boxes":[68],"fit":[70],"to":[72,77,91,96,110,114,132,146,155,199,202],"all":[73],"possible":[74],"leads":[76],"an":[78,130],"infeasible":[79],"number":[80],"prior":[82],"boxes.":[83],"We":[84,151,207],"explore":[85],"different":[87,101,226],"approach":[88],"propose":[90],"train":[92],"neural":[94],"network":[95,104],"compute":[97],"distance":[98,113,126,135,187],"maps":[99,127,188],"along":[100],"directions.":[102],"The":[103,138],"trained":[106],"at":[107],"each":[108],"pixel":[109],"predict":[111],"closest":[116],"object":[117],"contour":[118],"given":[121],"direction.":[122],"By":[123],"pooling":[124],"we":[128],"obtain":[129,147],"approximation":[131],"signed":[134],"function":[136],"(SDF).":[137],"SDF":[139],"may":[140],"then":[141],"thresholded":[143],"order":[145],"foreground-background":[149],"segmentation.":[150,206],"compare":[152],"foreground":[156,179],"segmentations":[157],"obtained":[158],"from":[159],"state-of-the-art":[161],"method":[164],"YOLACT.":[165],"On":[166],"COCO":[168],"dataset,":[169],"our":[170],"yields":[172],"higher":[174],"performance":[175],"terms":[177],"intersection":[180],"over":[181],"union":[182],"(IoU).":[183],"However,":[184],"while":[185],"contain":[189],"information":[190],"on":[191],"individual":[193],"instances,":[194],"it":[195,222],"not":[197],"straightforward":[198],"map":[200],"them":[201],"full":[204],"still":[208],"believe":[209],"idea":[212],"promising":[215],"research":[216],"direction":[217],"segmentation,":[220],"better":[223],"captures":[224],"found":[228],"real":[231],"world.":[232]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}