{"id":"https://openalex.org/W2550927680","doi":"https://doi.org/10.1109/ijcnn.2016.7727861","title":"Perception of noise in global illumination based on inductive learning","display_name":"Perception of noise in global illumination based on inductive learning","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2550927680","doi":"https://doi.org/10.1109/ijcnn.2016.7727861","mag":"2550927680"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5039463595","display_name":"Joseph Constantin","orcid":"https://orcid.org/0000-0002-7911-1218"},"institutions":[{"id":"https://openalex.org/I160368002","display_name":"Lebanese University","ror":"https://ror.org/05x6qnc69","country_code":"LB","type":"education","lineage":["https://openalex.org/I160368002"]}],"countries":["LB"],"is_corresponding":false,"raw_author_name":"Joseph Constantin","raw_affiliation_strings":["Facult\u00e9 des Sciences 2 Campus Fanar, Universit\u00e9 Libanaise, Jdeidet, Liban"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facult\u00e9 des Sciences 2 Campus Fanar, Universit\u00e9 Libanaise, Jdeidet, Liban","institution_ids":["https://openalex.org/I160368002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013968306","display_name":"Ibtissam Constantin","orcid":"https://orcid.org/0000-0002-4252-0819"},"institutions":[{"id":"https://openalex.org/I160368002","display_name":"Lebanese University","ror":"https://ror.org/05x6qnc69","country_code":"LB","type":"education","lineage":["https://openalex.org/I160368002"]}],"countries":["LB"],"is_corresponding":false,"raw_author_name":"Ibtissam Constantin","raw_affiliation_strings":["Facult\u00e9 des Sciences 2 Campus Fanar, Universit\u00e9 Libanaise, Jdeidet, Liban"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facult\u00e9 des Sciences 2 Campus Fanar, Universit\u00e9 Libanaise, Jdeidet, Liban","institution_ids":["https://openalex.org/I160368002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056859585","display_name":"Andr\u00e9 Bigand","orcid":"https://orcid.org/0000-0002-3165-5363"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andre Bigand","raw_affiliation_strings":["LISIC, ULCO, Calais Cedex, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LISIC, ULCO, Calais Cedex, France","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084523165","display_name":"Denis Hamad","orcid":"https://orcid.org/0000-0001-6000-8707"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Denis Hamad","raw_affiliation_strings":["LISIC, ULCO, Calais Cedex, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LISIC, ULCO, Calais Cedex, France","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.12220228,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"5021","last_page":"5028"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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/T11666","display_name":"Color Science and Applications","score":0.9984999895095825,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/global-illumination","display_name":"Global illumination","score":0.7892000675201416},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.7824083566665649},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7626064419746399},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6427454948425293},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5220520496368408},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5072014331817627},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5036768317222595},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4909132122993469},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4738326966762543},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.44133320450782776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4023040533065796},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32359176874160767},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23774683475494385},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1673136055469513}],"concepts":[{"id":"https://openalex.org/C89720835","wikidata":"https://www.wikidata.org/wiki/Q1531701","display_name":"Global illumination","level":3,"score":0.7892000675201416},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.7824083566665649},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7626064419746399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6427454948425293},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5220520496368408},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5072014331817627},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5036768317222595},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4909132122993469},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4738326966762543},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.44133320450782776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4023040533065796},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32359176874160767},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23774683475494385},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1673136055469513},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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":42,"referenced_works":["https://openalex.org/W54037559","https://openalex.org/W82181109","https://openalex.org/W1491891726","https://openalex.org/W1523663624","https://openalex.org/W1546820750","https://openalex.org/W1552232516","https://openalex.org/W1829569432","https://openalex.org/W1907223193","https://openalex.org/W1948311811","https://openalex.org/W1964927429","https://openalex.org/W1976867134","https://openalex.org/W2021762618","https://openalex.org/W2025025456","https://openalex.org/W2045717113","https://openalex.org/W2058237598","https://openalex.org/W2064076387","https://openalex.org/W2071788534","https://openalex.org/W2093129085","https://openalex.org/W2105143518","https://openalex.org/W2109943925","https://openalex.org/W2118103965","https://openalex.org/W2122369908","https://openalex.org/W2124036825","https://openalex.org/W2128733372","https://openalex.org/W2135002170","https://openalex.org/W2143631990","https://openalex.org/W2145313871","https://openalex.org/W2156929696","https://openalex.org/W2159590695","https://openalex.org/W2160387688","https://openalex.org/W2163612361","https://openalex.org/W2216045586","https://openalex.org/W2532320182","https://openalex.org/W3023411394","https://openalex.org/W3138218344","https://openalex.org/W3182211633","https://openalex.org/W4294576732","https://openalex.org/W6603356841","https://openalex.org/W6631400791","https://openalex.org/W6677848198","https://openalex.org/W6679930706","https://openalex.org/W6798736878"],"related_works":["https://openalex.org/W2397912953","https://openalex.org/W2124439461","https://openalex.org/W2367434614","https://openalex.org/W2099004500","https://openalex.org/W2077708435","https://openalex.org/W4376115546","https://openalex.org/W4243167425","https://openalex.org/W1992884771","https://openalex.org/W4206603469","https://openalex.org/W4388994528"],"abstract_inverted_index":{"Global":[0],"illumination":[1,89,177],"methods":[2,33],"are":[3,93,101,131,173],"used":[4],"to":[5,16,35,42,55,96,104,122,134,149,165,234],"simulate":[6],"lighting":[7],"in":[8,40,70,87,120],"3D":[9],"scenes.":[10],"They":[11],"provide":[12],"a":[13,37,48,65,76,111,115],"progressive":[14],"convergence":[15,50],"high":[17],"quality":[18,67,210],"photo-realistic":[19],"images":[20,72,119,130,157],"as":[21,75],"proved":[22],"by":[23,152],"Monte":[24],"Carlo":[25],"theory.":[26],"One":[27,81],"of":[28,31,118,137,169],"the":[29,45,53,126,138,154,159,167,200,207,220,229],"problem":[30],"such":[32],"is":[34,73,90,148,232],"determine":[36],"stopping":[38],"condition":[39],"order":[41,121,164],"decide":[43],"if":[44],"computation":[46],"reaches":[47],"satisfactory":[49],"which":[51],"allows":[52],"process":[54],"terminate.":[56],"In":[57,163],"this":[58,79],"paper,":[59],"an":[60],"inductive":[61],"learning":[62,85,127,208,222,236],"model":[63,69,209,214],"for":[64,78,84,141,158],"reduced-reference":[66],"assessment":[68],"large-scale":[71],"proposed":[74,213,230],"solution":[77],"problem.":[80],"key":[82],"issue":[83],"algorithms":[86],"global":[88,176],"that":[91,228],"they":[92,109],"very":[94,132],"efficient":[95,103],"learn":[97,105],"perceptual":[98],"features":[99],"but":[100],"less":[102],"stochastic":[106],"noise.":[107],"Moreover,":[108],"need":[110],"complete":[112],"framework":[113],"with":[114,179,186,192,219],"huge":[116],"number":[117],"train":[123],"and":[124,143,182,188,206,237,241],"evaluate":[125],"model.":[128,223],"These":[129],"difficult":[133],"obtain":[135],"because":[136],"time":[139],"required":[140],"modeling":[142],"scene":[144],"rendering.":[145,190],"The":[146,212,224],"idea":[147],"improve":[150],"performance":[151,168],"selecting":[153],"most":[155],"pertinent":[156],"noise":[160,183],"perception":[161],"algorithm.":[162],"generalize":[166,235],"our":[170],"approach,":[171],"experiments":[172],"conducted":[174],"on":[175],"scenes,":[178],"different":[180],"precision":[181],"levels,":[184],"computed":[185],"diffuse":[187],"specular":[189],"Compared":[191],"human":[193],"psychovisual":[194],"scores,":[195],"it":[196],"can":[197],"be":[198],"seen":[199],"good":[201],"consistency":[202],"between":[203],"these":[204],"scores":[205],"measures.":[211],"has":[215],"also":[216],"been":[217],"compared":[218],"SVM":[221],"obtained":[225],"results":[226],"show":[227],"method":[231],"powerful":[233],"gives":[238],"promising":[239],"efficiency":[240],"precision.":[242]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
