{"id":"https://openalex.org/W2910531615","doi":"https://doi.org/10.1109/tci.2019.2893820","title":"TransCut2: Transparent Object Segmentation From a Light-Field Image","display_name":"TransCut2: Transparent Object Segmentation From a Light-Field Image","publication_year":2019,"publication_date":"2019-01-17","ids":{"openalex":"https://openalex.org/W2910531615","doi":"https://doi.org/10.1109/tci.2019.2893820","mag":"2910531615"},"language":"en","primary_location":{"id":"doi:10.1109/tci.2019.2893820","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2019.2893820","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2334-0118","2573-0436"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Imaging","raw_type":"journal-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/A5101896494","display_name":"Yichao Xu","orcid":"https://orcid.org/0000-0002-9716-3599"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yichao Xu","raw_affiliation_strings":["Institute for Datability Science, Osaka University, Osaka, Japan"],"raw_orcid":"https://orcid.org/0000-0002-9716-3599","affiliations":[{"raw_affiliation_string":"Institute for Datability Science, Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065709581","display_name":"Hajime Nagahara","orcid":"https://orcid.org/0000-0003-1579-8767"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hajime Nagahara","raw_affiliation_strings":["Institute for Datability Science, Osaka University, Osaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Datability Science, Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046057343","display_name":"Atsushi Shimada","orcid":"https://orcid.org/0000-0002-3635-9336"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Atsushi Shimada","raw_affiliation_strings":["Kyushu University, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111450732","display_name":"Rin-ichiro Taniguchi","orcid":null},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Rin-ichiro Taniguchi","raw_affiliation_strings":["Kyushu University, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3607,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.81470549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"5","issue":"3","first_page":"465","last_page":"477"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9995999932289124,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9995999932289124,"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":0.9958000183105469,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.8546212315559387},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8464488387107849},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6644710302352905},{"id":"https://openalex.org/keywords/light-field","display_name":"Light field","score":0.6393018364906311},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5781207084655762},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5591607689857483},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5519095659255981},{"id":"https://openalex.org/keywords/cut","display_name":"Cut","score":0.45134469866752625},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.43557021021842957},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.42452430725097656}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.8546212315559387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8464488387107849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6644710302352905},{"id":"https://openalex.org/C48983235","wikidata":"https://www.wikidata.org/wiki/Q593161","display_name":"Light field","level":2,"score":0.6393018364906311},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5781207084655762},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5591607689857483},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5519095659255981},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.45134469866752625},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.43557021021842957},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.42452430725097656},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tci.2019.2893820","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2019.2893820","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2334-0118","2573-0436"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2432831563","display_name":"Computational Optical Imaging for Endscopic Surgery","funder_award_id":"17H06102","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W79315950","https://openalex.org/W156779288","https://openalex.org/W1184988332","https://openalex.org/W1484177357","https://openalex.org/W1585631804","https://openalex.org/W1903029394","https://openalex.org/W1969624871","https://openalex.org/W1991706125","https://openalex.org/W2000063966","https://openalex.org/W2017569774","https://openalex.org/W2017691720","https://openalex.org/W2021640751","https://openalex.org/W2027621340","https://openalex.org/W2033040413","https://openalex.org/W2055449329","https://openalex.org/W2102616341","https://openalex.org/W2102648530","https://openalex.org/W2104974755","https://openalex.org/W2107027687","https://openalex.org/W2111218333","https://openalex.org/W2113708607","https://openalex.org/W2113752546","https://openalex.org/W2114487471","https://openalex.org/W2119300483","https://openalex.org/W2121947440","https://openalex.org/W2123094455","https://openalex.org/W2124351162","https://openalex.org/W2128084987","https://openalex.org/W2128268941","https://openalex.org/W2131747574","https://openalex.org/W2133059825","https://openalex.org/W2135815156","https://openalex.org/W2136573752","https://openalex.org/W2137464770","https://openalex.org/W2140188952","https://openalex.org/W2145023731","https://openalex.org/W2150729352","https://openalex.org/W2155084314","https://openalex.org/W2156341850","https://openalex.org/W2162253476","https://openalex.org/W2163039610","https://openalex.org/W2164299365","https://openalex.org/W2175023734","https://openalex.org/W2200825767","https://openalex.org/W2293126239","https://openalex.org/W2737242453","https://openalex.org/W3144159205","https://openalex.org/W3211330693","https://openalex.org/W4214595485","https://openalex.org/W6635206423","https://openalex.org/W6675789117","https://openalex.org/W6680096826","https://openalex.org/W6680762990"],"related_works":["https://openalex.org/W4360784979","https://openalex.org/W3017192027","https://openalex.org/W2204605857","https://openalex.org/W1996489018","https://openalex.org/W2007664797","https://openalex.org/W2120981610","https://openalex.org/W3129669851","https://openalex.org/W2360759360","https://openalex.org/W3196005494","https://openalex.org/W2799097543"],"abstract_inverted_index":{"Transparent":[0],"object":[1,76],"segmentation":[2,37],"can":[3],"be":[4],"very":[5],"useful":[6],"in":[7,56],"computer":[8],"vision":[9],"applications.":[10],"However,":[11],"because":[12],"the":[13,51,67,74,81,88,96,113,128,136],"transparent":[14,75,114,133],"objects":[15,134],"borrow":[16],"texture":[17],"from":[18,105,135],"their":[19,27],"background":[20,137],"and":[21,53,80,109,116],"have":[22],"a":[23,44,57,70],"similar":[24],"appearance":[25],"to":[26,65,73,86,120],"surroundings,":[28],"they":[29],"are":[30],"not":[31],"handled":[32],"well":[33],"by":[34],"regular":[35],"image":[36],"methods.":[38],"In":[39],"this":[40],"paper,":[41],"we":[42],"propose":[43],"method":[45,130],"that":[46,127],"overcomes":[47],"these":[48,118],"problems":[49],"using":[50],"consistency":[52],"distortion":[54],"properties":[55],"light-field":[58,61],"image.":[59],"The":[60,124],"linearity":[62],"is":[63,84,93],"used":[64,85],"estimate":[66],"likelihood":[68],"of":[69],"pixel":[71,97],"belonging":[72],"or":[77],"Lambertian":[78],"background,":[79],"occlusion":[82,89],"detector":[83],"find":[87],"boundary.":[90],"Graph-cut":[91],"optimization":[92],"applied":[94],"for":[95,112],"labeling":[98],"problem.":[99],"We":[100],"acquire":[101],"light":[102],"field":[103],"datasets":[104,119],"both":[106],"camera":[107,111],"array":[108],"lenslet":[110],"object,":[115],"use":[117],"evaluate":[121],"our":[122],"method.":[123],"results":[125],"demonstrate":[126],"proposed":[129],"successfully":[131],"segments":[132],"under":[138],"various":[139],"conditions.":[140]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-08T01:25:22.217667","created_date":"2025-10-10T00:00:00"}
