{"id":"https://openalex.org/W2410325215","doi":"https://doi.org/10.1109/acpr.2015.7486557","title":"Robust road lane detection using extremal-region enhancement","display_name":"Robust road lane detection using extremal-region enhancement","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2410325215","doi":"https://doi.org/10.1109/acpr.2015.7486557","mag":"2410325215"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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/A5113800872","display_name":"Jingchen Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]},{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN","JP"],"is_corresponding":true,"raw_author_name":"Jingchen Gu","raw_affiliation_strings":["Department of Automation, Shanghai Jiao Tong University, China","Graduate School of Information, Production and Systems, Waseda University, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Shanghai Jiao Tong University, China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Graduate School of Information, Production and Systems, Waseda University, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011954673","display_name":"Qieshi Zhang","orcid":"https://orcid.org/0000-0001-6358-1840"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Qieshi Zhang","raw_affiliation_strings":["Faculty of Science and Engineering, Waseda University, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Science and Engineering, Waseda University, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066452160","display_name":"Sei\u2010ichiro Kamata","orcid":"https://orcid.org/0000-0002-1496-2417"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sei-ichiro Kamata","raw_affiliation_strings":["Graduate School of Information, Production and Systems, Waseda University, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information, Production and Systems, Waseda University, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113800872"],"corresponding_institution_ids":["https://openalex.org/I150744194","https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.628,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.76845771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998999834060669,"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":0.9998999834060669,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9976999759674072,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9923999905586243,"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-science","display_name":"Computer science","score":0.728230357170105},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.6525984406471252},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.641186535358429},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6376621127128601},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5953790545463562},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.5882080793380737},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.49717047810554504},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.4911138713359833},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.45055249333381653},{"id":"https://openalex.org/keywords/lane-departure-warning-system","display_name":"Lane departure warning system","score":0.4252476394176483},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34563201665878296},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32427698373794556},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.22974291443824768},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1770704984664917}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.728230357170105},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.6525984406471252},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.641186535358429},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6376621127128601},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5953790545463562},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.5882080793380737},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.49717047810554504},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.4911138713359833},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.45055249333381653},{"id":"https://openalex.org/C151043444","wikidata":"https://www.wikidata.org/wiki/Q28253","display_name":"Lane departure warning system","level":2,"score":0.4252476394176483},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34563201665878296},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32427698373794556},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.22974291443824768},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1770704984664917},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acpr.2015.7486557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1968258901","https://openalex.org/W1968411243","https://openalex.org/W1998771994","https://openalex.org/W2042105889","https://openalex.org/W2092272471","https://openalex.org/W2092282499","https://openalex.org/W2102049273","https://openalex.org/W2109618991","https://openalex.org/W2131076267","https://openalex.org/W2133059825","https://openalex.org/W2146575011","https://openalex.org/W2178624248","https://openalex.org/W6680096826"],"related_works":["https://openalex.org/W1579855068","https://openalex.org/W3202558484","https://openalex.org/W2183538373","https://openalex.org/W4249335891","https://openalex.org/W580929820","https://openalex.org/W4293765031","https://openalex.org/W3003346118","https://openalex.org/W617511802","https://openalex.org/W2028037572","https://openalex.org/W2315652488"],"abstract_inverted_index":{"Road":[0],"lane":[1,87,99],"detection":[2,17,37,40,100,119],"is":[3,69],"a":[4,50,96],"key":[5],"problem":[6],"in":[7,41,65,89],"advanced":[8],"driver-assistance":[9],"systems":[10],"(ADAS).":[11],"For":[12],"solving":[13],"this":[14,46],"problem,":[15],"vision-based":[16],"methods":[18],"are":[19,23,84],"widely":[20],"used":[21],"and":[22,38,73,78,106],"generally":[24],"focused":[25],"on":[26],"edge":[27,32,77],"information.":[28],"However,":[29],"only":[30],"using":[31,102],"information":[33,80],"leads":[34],"to":[35,71],"miss":[36],"error":[39],"various":[42],"road":[43,98],"conditions.":[44],"In":[45,92],"paper,":[47],"we":[48,94],"propose":[49],"neighbor-based":[51],"image":[52],"conversion":[53],"method,":[54],"called":[55],"extremal-region":[56],"enhancement.":[57],"The":[58,113],"proposed":[59],"method":[60],"enhances":[61],"the":[62,90,103,108],"white":[63,82],"lines":[64,83],"intensity,":[66],"hence":[67],"it":[68],"robust":[70,97],"shadows":[72],"illuminance":[74],"changes.":[75],"Both":[76],"shape":[79],"of":[81,122],"extracted":[85,104],"as":[86],"features":[88,105],"method.":[91],"addition,":[93],"implement":[95],"algorithm":[101],"improve":[107],"correctness":[109],"through":[110],"probability":[111],"tracking.":[112],"experimental":[114],"result":[115],"shows":[116],"an":[117],"average":[118],"rate":[120],"increase":[121],"13.2%":[123],"over":[124],"existing":[125],"works.":[126]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}