{"id":"https://openalex.org/W1526497706","doi":"https://doi.org/10.1109/ascc.2015.7244743","title":"High precision road segmentation for cover level of forward view estimation via stereo camera","display_name":"High precision road segmentation for cover level of forward view estimation via stereo camera","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1526497706","doi":"https://doi.org/10.1109/ascc.2015.7244743","mag":"1526497706"},"language":"en","primary_location":{"id":"doi:10.1109/ascc.2015.7244743","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ascc.2015.7244743","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 10th Asian Control Conference (ASCC)","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/A5112245295","display_name":"Siti Nor Khuzaimah Binti Amit","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Siti Nor Khuzaimah Binti Amit","raw_affiliation_strings":["Graduate School of Integrated Design Engineering, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Integrated Design Engineering, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070908826","display_name":"Yoshimitsu Aoki","orcid":"https://orcid.org/0000-0001-7361-0027"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshimitsu Aoki","raw_affiliation_strings":["Faculty of Science and Technology, Keio University, Yokohama, Japan","Faculty of Science and Technology, #N#Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Faculty of Science and Technology, #N#Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.04184979,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9988999962806702,"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/computer-vision","display_name":"Computer vision","score":0.726688027381897},{"id":"https://openalex.org/keywords/standard-illuminant","display_name":"Standard illuminant","score":0.7094234824180603},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7057475447654724},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7052456736564636},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6397655010223389},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5318602919578552},{"id":"https://openalex.org/keywords/shadow","display_name":"Shadow (psychology)","score":0.49890923500061035},{"id":"https://openalex.org/keywords/road-surface","display_name":"Road surface","score":0.43643391132354736},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13383471965789795}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.726688027381897},{"id":"https://openalex.org/C129112856","wikidata":"https://www.wikidata.org/wiki/Q375479","display_name":"Standard illuminant","level":2,"score":0.7094234824180603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7057475447654724},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7052456736564636},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6397655010223389},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5318602919578552},{"id":"https://openalex.org/C117797892","wikidata":"https://www.wikidata.org/wiki/Q286363","display_name":"Shadow (psychology)","level":2,"score":0.49890923500061035},{"id":"https://openalex.org/C2780042925","wikidata":"https://www.wikidata.org/wiki/Q1049667","display_name":"Road surface","level":2,"score":0.43643391132354736},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13383471965789795},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ascc.2015.7244743","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ascc.2015.7244743","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 10th Asian Control Conference (ASCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1704999350","https://openalex.org/W2099046646","https://openalex.org/W2106938558","https://openalex.org/W2115579991","https://openalex.org/W2129249398","https://openalex.org/W2131543790","https://openalex.org/W2135852591","https://openalex.org/W2149550213","https://openalex.org/W2150066425","https://openalex.org/W2161337244","https://openalex.org/W2166502676","https://openalex.org/W2168519618","https://openalex.org/W3004481906","https://openalex.org/W6637430524","https://openalex.org/W6681979930"],"related_works":["https://openalex.org/W4378218814","https://openalex.org/W2169565872","https://openalex.org/W2125997383","https://openalex.org/W2086480935","https://openalex.org/W24761535","https://openalex.org/W2141064695","https://openalex.org/W830216772","https://openalex.org/W164484593","https://openalex.org/W1917373540","https://openalex.org/W1522196789"],"abstract_inverted_index":{"To":[0],"develop":[1],"a":[2,58,95,133,209],"safe":[3],"Intelligent":[4],"Transportation":[5],"System":[6],"(ITS)":[7],"while":[8],"driving":[9],"on":[10,69,219],"unpredictable":[11],"curves":[12],"or":[13,50,233],"road":[14,18,48,54,70,88,99,127,134,143,158,163,179,193,238,251],"regions,":[15],"high":[16],"precision":[17,166],"segmentation":[19,71,100,164,180],"and":[20,148,172,202,205],"cover":[21,186,213],"level":[22,32,40,187,214],"of":[23,33,41,47,76,98,120,162,167,188,212,215],"forward":[24,34,189,216],"view":[25,35,190],"estimation":[26,201],"for":[27,184,199],"drivers":[28],"is":[29,36,74,90,101,129,137,154,181],"necessary.":[30],"Cover":[31],"defined":[37],"as":[38,72,170,175],"the":[39,45,62,65,77,85,117,142,220,230],"difficulty":[42],"in":[43,80,225,245],"predicting":[44],"dangerousness":[46],"edge":[49,55],"incoming":[51],"object":[52],"near":[53],"especially":[56,228],"at":[57,64],"curve":[59,200],"due":[60],"to":[61,84,111,125,131,140,156,208,237,242,247],"obstacles":[63,203],"surrounding.":[66],"We":[67],"focus":[68],"it":[73],"one":[75],"fundamental":[78],"steps":[79],"developing":[81],"ITS.":[82],"According":[83],"previous":[86],"studies,":[87],"region":[89,194],"not":[91],"segmented":[92,250],"precisely;":[93],"hence":[94,206],"new":[96],"method":[97,161],"introduced.":[102],"Input":[103],"images":[104],"had":[105],"undergone":[106],"illuminant":[107,121,234],"invariant":[108,122,235],"space":[109],"conversion":[110],"remove":[112],"shadow":[113],"regions":[114],"effectively.":[115],"Next,":[116],"bottom":[118],"part":[119],"image":[123,232,236],"(assumed":[124],"be":[126,196,243],"surface)":[128],"sampled":[130],"get":[132],"model,":[135],"which":[136],"then":[138],"applied":[139,155],"obtain":[141,157,248],"probability":[144,239],"image.":[145],"Lastly,":[146],"dilation":[147],"erosion":[149],"using":[150],"mathematical":[151],"morphology":[152],"operation":[153],"region.":[159,252],"Our":[160],"shows":[165],"0.73,":[168],"recall":[169],"0.82":[171],"F":[173],"measure":[174],"0.81.":[176],"High":[177],"Precision":[178],"very":[182],"important":[183],"better":[185,210],"estimation.":[191],"Segmented":[192],"can":[195],"fully":[197],"utilized":[198],"detection":[204],"lead":[207],"performance":[211],"view.":[217],"Based":[218],"results":[221],"obtained,":[222],"further":[223],"improvement":[224],"several":[226],"aspects,":[227],"from":[229],"input":[231],"image,":[240],"has":[241],"done":[244],"order":[246],"perfectly":[249]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
