{"id":"https://openalex.org/W2578366736","doi":"https://doi.org/10.1109/ipta.2016.7820960","title":"Fast growing hough forest as a stable model for object detection","display_name":"Fast growing hough forest as a stable model for object detection","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2578366736","doi":"https://doi.org/10.1109/ipta.2016.7820960","mag":"2578366736"},"language":"en","primary_location":{"id":"doi:10.1109/ipta.2016.7820960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2016.7820960","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-01451140","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083165413","display_name":"Antoine Tran","orcid":null},"institutions":[{"id":"https://openalex.org/I201181511","display_name":"\u00c9cole Nationale Sup\u00e9rieure de Techniques Avanc\u00e9es","ror":"https://ror.org/0309cs235","country_code":"FR","type":"education","lineage":["https://openalex.org/I201181511","https://openalex.org/I4210145102"]},{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Antoine Tran","raw_affiliation_strings":["U2IS, ENSTA ParisTech, Universit\u00e9 de Paris-Saclay, 828, Bd des Mar\u00e9chaux, 91762 Palaiseau CEDEX - France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"U2IS, ENSTA ParisTech, Universit\u00e9 de Paris-Saclay, 828, Bd des Mar\u00e9chaux, 91762 Palaiseau CEDEX - France","institution_ids":["https://openalex.org/I201181511","https://openalex.org/I204730241"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083582547","display_name":"Antoine Manzanera","orcid":"https://orcid.org/0000-0001-5718-411X"},"institutions":[{"id":"https://openalex.org/I201181511","display_name":"\u00c9cole Nationale Sup\u00e9rieure de Techniques Avanc\u00e9es","ror":"https://ror.org/0309cs235","country_code":"FR","type":"education","lineage":["https://openalex.org/I201181511","https://openalex.org/I4210145102"]},{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Antoine Manzanera","raw_affiliation_strings":["U2IS, ENSTA ParisTech Universite de Paris-Saclay, Palaiseau CEDEX, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"U2IS, ENSTA ParisTech Universite de Paris-Saclay, Palaiseau CEDEX, France","institution_ids":["https://openalex.org/I201181511","https://openalex.org/I204730241"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.9986000061035156,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9986000061035156,"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":0.9973999857902527,"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"}},{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hough-transform","display_name":"Hough transform","score":0.7572305202484131},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.649243950843811},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6415469646453857},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6359661221504211},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6274423599243164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6064351797103882},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5596087574958801},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5156001448631287},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5122043490409851},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.49267417192459106},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4875912070274353},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4442652463912964},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4386419653892517},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.43364134430885315},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.4140506088733673},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3357601761817932},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.22798827290534973},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11517000198364258}],"concepts":[{"id":"https://openalex.org/C200518788","wikidata":"https://www.wikidata.org/wiki/Q195076","display_name":"Hough transform","level":3,"score":0.7572305202484131},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.649243950843811},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6415469646453857},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6359661221504211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6274423599243164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6064351797103882},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5596087574958801},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5156001448631287},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5122043490409851},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.49267417192459106},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4875912070274353},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4442652463912964},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4386419653892517},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.43364134430885315},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.4140506088733673},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3357601761817932},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.22798827290534973},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11517000198364258},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ipta.2016.7820960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2016.7820960","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01451140v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01451140","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"The sixth International Conference on Image Processing Theory, Tools and Applications (IPTA'16), 2016, Oulu, Finland. pp.1 - 6, ⟨10.1109/IPTA.2016.7820960⟩","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-01451140v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01451140","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"The sixth International Conference on Image Processing Theory, Tools and Applications (IPTA'16), 2016, Oulu, Finland. pp.1 - 6, ⟨10.1109/IPTA.2016.7820960⟩","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1551725996","https://openalex.org/W1574125631","https://openalex.org/W1578026398","https://openalex.org/W1995444699","https://openalex.org/W2012330712","https://openalex.org/W2022686119","https://openalex.org/W2104241941","https://openalex.org/W2109200236","https://openalex.org/W2125337786","https://openalex.org/W2133059825","https://openalex.org/W2135512949","https://openalex.org/W2135931458","https://openalex.org/W2138302688","https://openalex.org/W2160978182","https://openalex.org/W2161969291","https://openalex.org/W2293733735","https://openalex.org/W2911964244","https://openalex.org/W6680096826","https://openalex.org/W6683411478"],"related_works":["https://openalex.org/W2030098947","https://openalex.org/W1974777989","https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2922437833","https://openalex.org/W4223892596","https://openalex.org/W4312696271","https://openalex.org/W2933098581","https://openalex.org/W2556125083"],"abstract_inverted_index":{"Hough":[0,6],"Forest":[1,10],"is":[2,20,140],"a":[3,64,69,106],"framework":[4,30],"combining":[5],"Transform":[7],"and":[8,25,126,147],"Random":[9],"for":[11,88],"object":[12],"detection.":[13],"The":[14,134],"purpose":[15],"of":[16,27,34,39,66,76,78,82,98,130,136,145],"the":[17,23,28,32,41,49,57,74,79,85,90,96,111,119,124,128,131,143],"present":[18],"paper":[19],"to":[21,105,116],"improve":[22],"efficiency":[24],"reliability":[26],"original":[29],"by":[31,44,62,150],"mean":[33],"two":[35],"contributions.":[36],"First,":[37],"instead":[38,81],"generating":[40],"image":[42,60,99],"samples":[43],"drawing":[45],"patches":[46,67],"randomly":[47],"from":[48,68,110],"training":[50,120],"set,":[51],"we":[52,101],"bias":[53],"this":[54,137],"step":[55],"toward":[56],"most":[58],"relevant":[59],"content":[61],"selecting":[63],"proportion":[65],"geometrical":[70],"criterion.":[71],"Second,":[72],"during":[73],"creation":[75],"non-leaf-nodes":[77],"trees,":[80],"sampling":[83],"uniformly":[84],"parameter":[86],"space":[87],"choosing":[89],"binary":[91],"tests":[92],"aimed":[93],"at":[94],"splitting":[95],"set":[97],"samples,":[100],"choose":[102],"them":[103],"according":[104],"probability":[107],"map":[108],"constructed":[109],"sample":[112],"set.":[113],"We":[114],"aim":[115],"drastically":[117],"reduce":[118],"time":[121],"without":[122],"impacting":[123],"accuracy,":[125],"decreasing":[127],"variability":[129],"produced":[132],"detectors.":[133],"interest":[135],"improved":[138],"model":[139],"shown":[141],"in":[142],"context":[144],"car":[146],"pedestrian":[148],"detection":[149],"evaluating":[151],"it":[152],"on":[153],"academic":[154],"datasets.":[155]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}