{"id":"https://openalex.org/W2555147445","doi":"https://doi.org/10.1109/iccp.2016.7737146","title":"Optimizing Census-based Semi Global Matching by genetic algorithms","display_name":"Optimizing Census-based Semi Global Matching by genetic algorithms","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2555147445","doi":"https://doi.org/10.1109/iccp.2016.7737146","mag":"2555147445"},"language":"en","primary_location":{"id":"doi:10.1109/iccp.2016.7737146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp.2016.7737146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP)","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/A5079231009","display_name":"Vlad\u2013Cristian Miclea","orcid":"https://orcid.org/0000-0002-5781-5433"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Vlad-Cristian Miclea","raw_affiliation_strings":["Technical University of Cluj-Napoca"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047954457","display_name":"Sergiu Nedevschi","orcid":"https://orcid.org/0000-0003-2018-4647"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Sergiu Nedevschi","raw_affiliation_strings":["Technical University of Cluj-Napoca"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca","institution_ids":["https://openalex.org/I158333966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I158333966"],"apc_list":null,"apc_paid":null,"fwci":0.3315,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67361934,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"8048","issue":null,"first_page":"193","last_page":"198"},"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":0.9984999895095825,"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":0.9973999857902527,"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/census","display_name":"Census","score":0.9081932306289673},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7155748009681702},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.674325704574585},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6507644653320312},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5516899824142456},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5416813492774963},{"id":"https://openalex.org/keywords/center","display_name":"Center (category theory)","score":0.42540544271469116},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3810965418815613},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3557436466217041},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2890905737876892},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22627714276313782},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.19504743814468384},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18074917793273926},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.08961978554725647}],"concepts":[{"id":"https://openalex.org/C52130261","wikidata":"https://www.wikidata.org/wiki/Q39825","display_name":"Census","level":3,"score":0.9081932306289673},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7155748009681702},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.674325704574585},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6507644653320312},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5516899824142456},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5416813492774963},{"id":"https://openalex.org/C2779463800","wikidata":"https://www.wikidata.org/wiki/Q5062222","display_name":"Center (category theory)","level":2,"score":0.42540544271469116},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3810965418815613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3557436466217041},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2890905737876892},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22627714276313782},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.19504743814468384},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18074917793273926},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.08961978554725647},{"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},{"id":"https://openalex.org/C8010536","wikidata":"https://www.wikidata.org/wiki/Q160398","display_name":"Crystallography","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccp.2016.7737146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp.2016.7737146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP)","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":17,"referenced_works":["https://openalex.org/W83224863","https://openalex.org/W1674866864","https://openalex.org/W1922120546","https://openalex.org/W1998119098","https://openalex.org/W2014888258","https://openalex.org/W2028365123","https://openalex.org/W2032339054","https://openalex.org/W2048679181","https://openalex.org/W2062612903","https://openalex.org/W2063404224","https://openalex.org/W2083911216","https://openalex.org/W2104974755","https://openalex.org/W2117248802","https://openalex.org/W2124109840","https://openalex.org/W2140836349","https://openalex.org/W2150066425","https://openalex.org/W2156982683"],"related_works":["https://openalex.org/W2128472366","https://openalex.org/W621243299","https://openalex.org/W5594354","https://openalex.org/W4244351752","https://openalex.org/W2601163983","https://openalex.org/W2364090708","https://openalex.org/W2145323372","https://openalex.org/W1512152715","https://openalex.org/W2009948611","https://openalex.org/W2182026161"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2],"shown":[3],"a":[4,16,46,109],"great":[5],"progress":[6],"in":[7,87,108],"self-driving":[8],"vehicles":[9],"and":[10,53,71,82,101,138,144],"stereovision":[11],"has":[12],"proven":[13],"to":[14,50,55,113],"be":[15,106],"key":[17],"aspect":[18],"towards":[19],"this":[20],"goal.":[21],"Semi-Global":[22],"Matching":[23],"(SGM)":[24],"algorithm":[25],"is":[26,42],"among":[27],"the":[28,67,72,76,102,115],"best":[29,73,103],"stereo":[30],"solutions,":[31],"capable":[32],"of":[33,117],"producing":[34],"reliable":[35],"results":[36],"at":[37],"reasonable":[38],"cost.":[39],"Census":[40,69,142],"transform":[41],"generally":[43],"preferred":[44],"as":[45,127,129],"cost":[47],"metric":[48],"due":[49],"its":[51],"robustness":[52],"invariance":[54],"lighting":[56],"conditions.":[57],"This":[58],"paper":[59],"proposes":[60],"an":[61],"original":[62],"methodology":[63],"for":[64,75,141],"finding":[65],"both":[66],"optimal":[68],"mask":[70],"values":[74],"penalties":[77],"P":[78,83],"<sub":[79,84],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[80,85],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[81],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sub>":[86],"SGM":[88],"by":[89],"using":[90],"genetic":[91],"algorithms":[92],"(GA).":[93],"The":[94],"obtained":[95],"census":[96,112,134],"masks":[97],"are":[98],"thoroughly":[99],"analyzed":[100],"ones":[104],"can":[105],"combined":[107],"weighted":[110,132],"center-symmetric":[111,133,139],"increase":[114],"performance":[116],"SGM.":[118,145],"Kitti":[119],"test":[120],"cases":[121],"show":[122],"that":[123],"our":[124,130],"GA-based":[125],"censuses":[126],"well":[128],"novel":[131],"outperform":[135],"dense,":[136],"sparse":[137],"counterparts":[140],"only":[143]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
