{"id":"https://openalex.org/W3003191127","doi":"https://doi.org/10.1117/12.2556671","title":"A deep interactive segmentation method with user interaction-based attention module and polar transformation","display_name":"A deep interactive segmentation method with user interaction-based attention module and polar transformation","publication_year":2020,"publication_date":"2020-01-31","ids":{"openalex":"https://openalex.org/W3003191127","doi":"https://doi.org/10.1117/12.2556671","mag":"3003191127"},"language":"en","primary_location":{"id":"doi:10.1117/12.2556671","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2556671","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Twelfth International Conference on Machine Vision (ICMV 2019)","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/A5112348995","display_name":"Jee-Young Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jee-Young Sun","raw_affiliation_strings":["Korea Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Korea Univ. (Korea, Republic of)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100444708","display_name":"Sang\u2010Won Lee","orcid":"https://orcid.org/0000-0002-5042-0084"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sang-Won Lee","raw_affiliation_strings":["Korea Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Korea Univ. (Korea, Republic of)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103887863","display_name":"Ye-Won Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye-Won Kim","raw_affiliation_strings":["Korea Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Korea Univ. (Korea, Republic of)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112502237","display_name":"Bo-Sang Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131320","display_name":"LG (South Korea)","ror":"https://ror.org/03ddh2c27","country_code":"KR","type":"company","lineage":["https://openalex.org/I4210131320"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Bo-Sang Kim","raw_affiliation_strings":["LG Electronics (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"LG Electronics (Korea, Republic of)","institution_ids":["https://openalex.org/I4210131320"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020023785","display_name":"Sung-Jea Ko","orcid":"https://orcid.org/0000-0002-4875-7091"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sung-Jea Ko","raw_affiliation_strings":["Korea Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Korea Univ. (Korea, Republic of)","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5112348995"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.01035155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"20","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9997000098228455,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9997000098228455,"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":0.9983999729156494,"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":0.9979000091552734,"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/computer-science","display_name":"Computer science","score":0.8543232679367065},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.785220742225647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6990069150924683},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.549734354019165},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5136474967002869},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49478286504745483},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4940955936908722},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4698549509048462},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4257740378379822},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.41241344809532166},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.41118061542510986}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8543232679367065},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.785220742225647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6990069150924683},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.549734354019165},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5136474967002869},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49478286504745483},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4940955936908722},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4698549509048462},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4257740378379822},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.41241344809532166},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.41118061542510986},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2556671","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2556671","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Twelfth International Conference on Machine Vision (ICMV 2019)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2185902295","https://openalex.org/W2103507220"],"abstract_inverted_index":{"Interactive":[0],"segmentation":[1,30,61,71,131,163,172,183],"that":[2,142,168],"extracts":[3],"a":[4,49,112,127],"specific":[5],"foreground":[6],"selected":[7],"by":[8],"the":[9,34,82,93,103,117,146,162,169,177],"user":[10,40,51,75,100,138],"input":[11],"is":[12,174],"widely":[13],"employed":[14],"in":[15,180],"many":[16],"user-interactive":[17],"applications":[18],"such":[19],"as":[20,111],"image":[21],"editing":[22],"and":[23,136,185],"ground-truth":[24],"labeling.":[25],"In":[26,150],"general,":[27],"most":[28],"interactive":[29,60,65,87,105,130,171],"methods":[31],"iteratively":[32],"refine":[33,116],"previously":[35],"obtained":[36],"result":[37],"using":[38],"additional":[39],"interactions":[41,76],"because":[42],"they":[43],"often":[44],"produce":[45],"unsatisfactory":[46],"results":[47,166],"with":[48,73],"single":[50],"input.":[52],"A":[53],"recently":[54],"developed":[55],"convolutional":[56],"neural":[57],"network":[58],"(CNN)-based":[59],"method":[62,132,173],"called":[63],"deep":[64,86,104,128],"object":[66,88,106],"selection":[67,89,107],"has":[68],"achieved":[69],"high":[70],"accuracy":[72,184],"fewer":[74],"than":[77],"earlier":[78],"non-CNN-based":[79],"approaches.":[80],"However,":[81],"computational":[83,186],"efficiency":[84],"of":[85,182],"deteriorates":[90],"due":[91],"to":[92,115,155,159,176],"repetitive":[94,147],"feature":[95,148],"extraction":[96],"stage":[97],"for":[98],"each":[99],"interaction.":[101],"Furthermore,":[102],"requires":[108],"graph":[109],"cut":[110],"post-processing":[113],"step":[114],"boundary":[118],"segments.":[119],"To":[120],"solve":[121],"this":[122,124],"problem,":[123],"paper":[125],"presents":[126],"CNN-based":[129],"employing":[133],"an":[134],"effective":[135],"simple":[137],"interaction-based":[139],"attention":[140],"module":[141],"does":[143],"not":[144],"require":[145],"extraction.":[149],"addition,":[151],"we":[152],"adopt":[153],"Cartesian":[154],"polar":[156],"coordinate":[157],"transformation":[158],"further":[160],"improve":[161],"performance.":[164],"Experimental":[165],"demonstrate":[167],"proposed":[170],"superior":[175],"conventional":[178],"ones":[179],"terms":[181],"efficiency.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}