{"id":"https://openalex.org/W2964276142","doi":"https://doi.org/10.1109/icpr.2016.7899914","title":"Deep structured-output regression learning for computational color constancy","display_name":"Deep structured-output regression learning for computational color constancy","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2964276142","doi":"https://doi.org/10.1109/icpr.2016.7899914","mag":"2964276142"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7899914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899914","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5110643431","display_name":"Yanlin Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]},{"id":"https://openalex.org/I4210121626","display_name":"Signal Processing (United States)","ror":"https://ror.org/021gzyw51","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121626"]},{"id":"https://openalex.org/I4210133110","display_name":"Tampere University","ror":null,"country_code":"FI","type":null,"lineage":["https://openalex.org/I4210133110"]}],"countries":["FI","US"],"is_corresponding":false,"raw_author_name":"Yanlin Qian","raw_affiliation_strings":["Department of Signal Processing, Tampere University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Signal Processing, Tampere University of Technology","institution_ids":["https://openalex.org/I166825849","https://openalex.org/I4210121626","https://openalex.org/I4210133110"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100649535","display_name":"Ke Chen","orcid":"https://orcid.org/0000-0003-0928-5199"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]},{"id":"https://openalex.org/I4210121626","display_name":"Signal Processing (United States)","ror":"https://ror.org/021gzyw51","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121626"]},{"id":"https://openalex.org/I4210133110","display_name":"Tampere University","ror":null,"country_code":"FI","type":null,"lineage":["https://openalex.org/I4210133110"]}],"countries":["FI","US"],"is_corresponding":false,"raw_author_name":"Ke Chen","raw_affiliation_strings":["Department of Signal Processing, Tampere University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Signal Processing, Tampere University of Technology","institution_ids":["https://openalex.org/I166825849","https://openalex.org/I4210121626","https://openalex.org/I4210133110"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054822570","display_name":"Joni\u2010Kristian K\u00e4m\u00e4r\u00e4inen","orcid":"https://orcid.org/0000-0002-5801-4371"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]},{"id":"https://openalex.org/I4210121626","display_name":"Signal Processing (United States)","ror":"https://ror.org/021gzyw51","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121626"]},{"id":"https://openalex.org/I4210133110","display_name":"Tampere University","ror":null,"country_code":"FI","type":null,"lineage":["https://openalex.org/I4210133110"]}],"countries":["FI","US"],"is_corresponding":false,"raw_author_name":"Joni-Kristian Kamarainen","raw_affiliation_strings":["Department of Signal Processing, Tampere University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Signal Processing, Tampere University of Technology","institution_ids":["https://openalex.org/I166825849","https://openalex.org/I4210121626","https://openalex.org/I4210133110"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030742579","display_name":"Jarno Nikkanen","orcid":"https://orcid.org/0000-0003-3801-7564"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jarno Nikkanen","raw_affiliation_strings":["Intel, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intel, Finland","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007656938","display_name":"Ji\u0159\u0131\u0301 Matas","orcid":"https://orcid.org/0000-0003-0863-4844"},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Jiri Matas","raw_affiliation_strings":["Center for Machine Perception, Czech Technical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Machine Perception, Czech Technical University","institution_ids":["https://openalex.org/I44504214"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.1106,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.99132228,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"1","issue":null,"first_page":"1899","last_page":"1904"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11666","display_name":"Color Science and Applications","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11666","display_name":"Color Science and Applications","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9962000250816345,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9901999831199646,"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/benchmark","display_name":"Benchmark (surveying)","score":0.8599745631217957},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7271457314491272},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.6646397709846497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6618261933326721},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6557591557502747},{"id":"https://openalex.org/keywords/color-constancy","display_name":"Color constancy","score":0.6450330018997192},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4828367829322815},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4704994857311249},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4360863268375397},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43531349301338196},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43483343720436096},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2603510916233063},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17815440893173218},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16351142525672913},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14443519711494446}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8599745631217957},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7271457314491272},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.6646397709846497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6618261933326721},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6557591557502747},{"id":"https://openalex.org/C187888035","wikidata":"https://www.wikidata.org/wiki/Q2563885","display_name":"Color constancy","level":3,"score":0.6450330018997192},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4828367829322815},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4704994857311249},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4360863268375397},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43531349301338196},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43483343720436096},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2603510916233063},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17815440893173218},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16351142525672913},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14443519711494446},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2016.7899914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899914","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320315323","display_name":"Technology Agency of the Czech Republic","ror":null},{"id":"https://openalex.org/F4320321108","display_name":"Academy of Finland","ror":"https://ror.org/05k73zm37"},{"id":"https://openalex.org/F4320321855","display_name":"Tekes","ror":"https://ror.org/02ag8cq23"},{"id":"https://openalex.org/F4320330293","display_name":"CSC \u2013 IT Center for Science","ror":"https://ror.org/04m8m1253"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W155265210","https://openalex.org/W759139936","https://openalex.org/W1489630993","https://openalex.org/W1491192762","https://openalex.org/W1530411632","https://openalex.org/W1534426948","https://openalex.org/W1536151268","https://openalex.org/W1553810758","https://openalex.org/W1686810756","https://openalex.org/W1894971994","https://openalex.org/W1925897490","https://openalex.org/W1960752652","https://openalex.org/W1964357740","https://openalex.org/W1966855663","https://openalex.org/W2013765095","https://openalex.org/W2021859388","https://openalex.org/W2031248101","https://openalex.org/W2052090926","https://openalex.org/W2055535946","https://openalex.org/W2061845203","https://openalex.org/W2068294844","https://openalex.org/W2074402838","https://openalex.org/W2075875861","https://openalex.org/W2097117768","https://openalex.org/W2100001370","https://openalex.org/W2107558608","https://openalex.org/W2108303106","https://openalex.org/W2111963040","https://openalex.org/W2115257219","https://openalex.org/W2122347864","https://openalex.org/W2128059302","https://openalex.org/W2139698815","https://openalex.org/W2147933509","https://openalex.org/W2151861336","https://openalex.org/W2154264220","https://openalex.org/W2155138846","https://openalex.org/W2163605009","https://openalex.org/W2169488311","https://openalex.org/W2171773573","https://openalex.org/W2287042286","https://openalex.org/W3023527190","https://openalex.org/W4377995851","https://openalex.org/W6629306003","https://openalex.org/W6629510997","https://openalex.org/W6631941535","https://openalex.org/W6631991694","https://openalex.org/W6632957422","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6685048874","https://openalex.org/W6776651637","https://openalex.org/W6852758018"],"related_works":["https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W2312753042","https://openalex.org/W3186837933","https://openalex.org/W1969346022","https://openalex.org/W2368989808","https://openalex.org/W2034959125","https://openalex.org/W2355687852","https://openalex.org/W3174513558"],"abstract_inverted_index":{"The":[0,21],"color":[1],"constancy":[2],"problem":[3],"is":[4],"addressed":[5],"by":[6],"structured-output":[7],"regression":[8],"on":[9,50,72],"the":[10,13,24,38,51,67,70,73],"values":[11],"of":[12,16,69],"fully-connected":[14,40],"layers":[15],"a":[17],"convolutional":[18],"neural":[19],"network.":[20],"AlexNet":[22],"and":[23,28,43,55],"VGG":[25,29],"are":[26],"considered":[27],"slightly":[30],"outperformed":[31],"AlexNet.":[32],"Best":[33],"results":[34],"were":[35],"obtained":[36],"with":[37,44],"first":[39],"\u201cfc6\u201d":[41],"layer":[42],"multi-output":[45],"support":[46],"vector":[47],"regression.":[48],"Experiments":[49],"SFU":[52,74],"Color":[53],"Checker":[54],"Indoor":[56],"Dataset":[57],"benchmarks":[58],"demonstrate":[59],"that":[60],"our":[61],"method":[62],"achieves":[63],"competitive":[64],"performance,":[65],"outperforming":[66],"state":[68],"art":[71],"indoor":[75],"benchmark.":[76]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
