{"id":"https://openalex.org/W2034492858","doi":"https://doi.org/10.1109/icip.2012.6466895","title":"Robust classification of traffic signs using multi-view cues","display_name":"Robust classification of traffic signs using multi-view cues","publication_year":2012,"publication_date":"2012-09-01","ids":{"openalex":"https://openalex.org/W2034492858","doi":"https://doi.org/10.1109/icip.2012.6466895","mag":"2034492858"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2012.6466895","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2012.6466895","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 19th IEEE International Conference on Image Processing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5050073968","display_name":"Lykele Hazelhoff","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Lykele Hazelhoff","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062997053","display_name":"Ivo Creusen","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ivo Creusen","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029008213","display_name":"Peter H. N. de With","orcid":"https://orcid.org/0000-0002-7639-7716"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Peter H.N. de With","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, Netherlands","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I83019370"],"apc_list":null,"apc_paid":null,"fwci":0.9011,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.75666698,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"2012","issue":null,"first_page":"457","last_page":"460"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9980999827384949,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9980999827384949,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9977999925613403,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9962000250816345,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7348335385322571},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.7348082065582275},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.7261891961097717},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6003512740135193},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5558687448501587},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.47513630986213684},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4715335965156555},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45379483699798584},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.42114073038101196},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.4184218943119049},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3293466567993164},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.26891326904296875},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15958988666534424}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7348335385322571},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.7348082065582275},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.7261891961097717},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6003512740135193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5558687448501587},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.47513630986213684},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4715335965156555},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45379483699798584},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.42114073038101196},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.4184218943119049},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3293466567993164},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26891326904296875},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15958988666534424},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/icip.2012.6466895","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2012.6466895","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 19th IEEE International Conference on Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/10919370-d58e-4a04-b198-3ecbd12d871c","is_oa":false,"landing_page_url":"https://research.tue.nl/en/publications/10919370-d58e-4a04-b198-3ecbd12d871c","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Hazelhoff, L, Creusen, I M & With, de, P H N 2012, Robust classification of traffic signs using multi-view cues. in Proceedings of the 19th International Conference on Image Processing (ICIP) 2012, 30 September - 3 October 2012, Orlando, Florida. Institute of Electrical and Electronics Engineers, Piscataway, pp. 457-460, 19th IEEE International Conference on Image Processing (ICIP 2012), Lake Buena Vista, FL, Florida, United States, 30/09/12. https://doi.org/10.1109/ICIP.2012.6466895","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.300.416","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://vca.ele.tue.nl/publications/data/LHazelhoff2012c.pdf","raw_type":"text"},{"id":"pmh:749311","is_oa":false,"landing_page_url":"http://library.tue.nl/csp/dare/LinkToRepository.csp?recordnumber=749311","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"pmh:oai:library.tue.nl:749311","is_oa":false,"landing_page_url":"http://repository.tue.nl/749311","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"pmh:tue:oai:pure.tue.nl:publications/10919370-d58e-4a04-b198-3ecbd12d871c","is_oa":false,"landing_page_url":"https://research.tue.nl/nl/publications/10919370-d58e-4a04-b198-3ecbd12d871c","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the 19th International Conference on Image Processing (ICIP) 2012, 30 September - 3 October 2012, Orlando, Florida, 457 - 460","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1625255723","https://openalex.org/W1978736542","https://openalex.org/W2001654224","https://openalex.org/W2034627636","https://openalex.org/W2110086022","https://openalex.org/W2113650053","https://openalex.org/W2136094925","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2169810643","https://openalex.org/W6636494156"],"related_works":["https://openalex.org/W2547328273","https://openalex.org/W3122938442","https://openalex.org/W2025206082","https://openalex.org/W746432122","https://openalex.org/W174788178","https://openalex.org/W637596653","https://openalex.org/W643638081","https://openalex.org/W1582770384","https://openalex.org/W2369781462","https://openalex.org/W564764569"],"abstract_inverted_index":{"Traffic":[0],"sign":[1,44,54,76,99],"inventories":[2],"are":[3],"created":[4],"for":[5,66],"road":[6],"safety":[7],"and":[8],"maintenance":[9],"based":[10,80],"on":[11,81],"street-level":[12],"panoramic":[13],"images.":[14],"Due":[15],"to":[16,70,89,125],"the":[17,25,33,39,42,71,75,98,118,126],"large":[18,21],"capturing":[19],"interval,":[20],"viewpoint":[22,30,68],"deviations":[23],"between":[24],"different":[26],"capturings":[27],"occur.":[28],"These":[29],"variations":[31],"complicate":[32],"classification":[34,72],"procedure,":[35,73],"which":[36,93],"aims":[37],"at":[38],"selection":[40],"of":[41,47,51],"correct":[43],"type,":[45],"out":[46],"a":[48,63,90],"high":[49],"number":[50],"nearly":[52],"similar":[53],"types,":[55],"typically":[56],"resulting":[57],"in":[58],"misclassifications.":[59],"This":[60],"paper":[61],"describes":[62],"novel":[64],"approach":[65,116],"incorporating":[67],"information":[69],"where":[74],"orientation":[77],"is":[78,87,94,101],"estimated":[79],"dense":[82],"matching.":[83],"Afterwards,":[84],"each":[85],"sample":[86],"corrected":[88],"frontal":[91],"viewpoint,":[92],"then":[95],"classified.":[96],"Finally,":[97],"type":[100],"obtained":[102],"by":[103,121],"weighted":[104],"voting.":[105],"Large-scale":[106],"experiments":[107],"including":[108],"2,":[109],"224":[110],"traffic":[111],"signs":[112],"show":[113],"that":[114],"this":[115],"reduces":[117],"misclassification":[119],"rate":[120],"about":[122],"33%":[123],"compared":[124],"single-view":[127],"case.":[128]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}