{"id":"https://openalex.org/W2789955133","doi":"https://doi.org/10.1109/icip.2017.8296736","title":"Unsupervised stereo matching using correspondence consistency","display_name":"Unsupervised stereo matching using correspondence consistency","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2789955133","doi":"https://doi.org/10.1109/icip.2017.8296736","mag":"2789955133"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296736","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296736","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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/A5054174499","display_name":"Sunghun Joung","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Sunghun Joung","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085363061","display_name":"Seungryong Kim","orcid":"https://orcid.org/0000-0003-2927-6273"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungryong Kim","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054888241","display_name":"Bumsub Ham","orcid":"https://orcid.org/0000-0002-3443-8161"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Bumsub Ham","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111658039","display_name":"Kwanghoon Sohn","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwanghoon Sohn","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5054174499"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.3641,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.70559436,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2518","last_page":"2522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":1.0,"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":1.0,"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/T11019","display_name":"Image Enhancement Techniques","score":0.996399998664856,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.996399998664856,"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/epipolar-geometry","display_name":"Epipolar geometry","score":0.78132164478302},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7810078263282776},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7680291533470154},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.708722710609436},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6503232717514038},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6385636925697327},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6258180141448975},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5134092569351196},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49243101477622986},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48991554975509644},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37605369091033936},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33334022760391235},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1554882526397705},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11603572964668274}],"concepts":[{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.78132164478302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7810078263282776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7680291533470154},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.708722710609436},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6503232717514038},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6385636925697327},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6258180141448975},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5134092569351196},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49243101477622986},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48991554975509644},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37605369091033936},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33334022760391235},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1554882526397705},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11603572964668274},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296736","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296736","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1520997877","https://openalex.org/W1674866864","https://openalex.org/W1772650917","https://openalex.org/W1803059841","https://openalex.org/W1904727610","https://openalex.org/W1955055330","https://openalex.org/W1963882359","https://openalex.org/W1970714520","https://openalex.org/W2033743519","https://openalex.org/W2040720132","https://openalex.org/W2072923658","https://openalex.org/W2104974755","https://openalex.org/W2117248802","https://openalex.org/W2128357735","https://openalex.org/W2133255058","https://openalex.org/W2148534289","https://openalex.org/W2171740948","https://openalex.org/W2214868166","https://openalex.org/W2300779272","https://openalex.org/W2435623039","https://openalex.org/W2440384215","https://openalex.org/W2520707372","https://openalex.org/W2565639579","https://openalex.org/W2950974964","https://openalex.org/W2951234442","https://openalex.org/W2962891704","https://openalex.org/W2963502507","https://openalex.org/W6638039622","https://openalex.org/W6660783294","https://openalex.org/W6685261749","https://openalex.org/W6697658144","https://openalex.org/W6764386301"],"related_works":["https://openalex.org/W132764016","https://openalex.org/W2148054235","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"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs)":[4],"have":[5],"shown":[6],"revolutionary":[7],"performance":[8],"improvements":[9],"for":[10,55],"matching":[11,56],"cost":[12,57,112],"computation":[13,58,113],"in":[14,25,59,90],"stereo":[15,72,80],"matching.":[16],"However,":[17],"conventional":[18],"CNN-based":[19],"approaches":[20],"to":[21,50,99],"learn":[22,51,100],"the":[23,76,87,101,105,122],"network":[24,94],"a":[26,30,47,52,110],"supervised":[27,127],"manner":[28],"require":[29],"large":[31],"number":[32],"of":[33],"ground-truth":[34],"disparity":[35],"maps,":[36],"which":[37],"limits":[38],"their":[39],"applicability.":[40],"To":[41,103],"overcome":[42],"this":[43],"limitation,":[44],"we":[45,107],"present":[46],"novel":[48],"framework":[49],"CNNs":[53],"architecture":[54],"an":[60,66],"unsupervised":[61],"manner.":[62],"Our":[63],"method":[64,85,120],"leverages":[65],"image":[67],"domain":[68],"learning":[69,128],"combined":[70],"with":[71],"epipolar":[73],"constraints.":[74],"Exploiting":[75],"correspondence":[77],"consistency":[78],"between":[79],"images":[81],"as":[82],"supervision,":[83],"our":[84,119],"selects":[86],"training":[88,95],"samples":[89],"each":[91],"iteration":[92],"during":[93],"and":[96],"uses":[97],"them":[98],"network.":[102],"boost":[104],"performance,":[106],"also":[108],"propose":[109],"multi-scale":[111],"scheme.":[114],"Experimental":[115],"results":[116],"show":[117],"that":[118],"outperforms":[121],"state-of-the-art":[123],"methods":[124,130],"including":[125],"even":[126],"based":[129],"on":[131],"various":[132],"benchmarks.":[133]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}