{"id":"https://openalex.org/W3133537163","doi":"https://doi.org/10.1109/iceic51217.2021.9369802","title":"An Efficient Recognition Method for Object Detection System","display_name":"An Efficient Recognition Method for Object Detection System","publication_year":2021,"publication_date":"2021-01-31","ids":{"openalex":"https://openalex.org/W3133537163","doi":"https://doi.org/10.1109/iceic51217.2021.9369802","mag":"3133537163"},"language":"en","primary_location":{"id":"doi:10.1109/iceic51217.2021.9369802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic51217.2021.9369802","pdf_url":null,"source":{"id":"https://openalex.org/S4306498844","display_name":"2021 International Conference on Electronics, Information, and Communication (ICEIC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Electronics, Information, and Communication (ICEIC)","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/A5100747686","display_name":"Kyungmin Lee","orcid":"https://orcid.org/0000-0001-6018-4014"},"institutions":[{"id":"https://openalex.org/I4210107562","display_name":"Semyung University","ror":"https://ror.org/01d100w34","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210107562"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kyung-Min Lee","raw_affiliation_strings":["School of Computer, Semyung University, Chungbuk, Korea"],"affiliations":[{"raw_affiliation_string":"School of Computer, Semyung University, Chungbuk, Korea","institution_ids":["https://openalex.org/I4210107562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061468707","display_name":"Chi-Ho Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107562","display_name":"Semyung University","ror":"https://ror.org/01d100w34","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210107562"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chi-ho Lin","raw_affiliation_strings":["School of Computer, Semyung University, Chungbuk, Korea"],"affiliations":[{"raw_affiliation_string":"School of Computer, Semyung University, Chungbuk, Korea","institution_ids":["https://openalex.org/I4210107562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100747686"],"corresponding_institution_ids":["https://openalex.org/I4210107562"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03938619,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9362000226974487,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9362000226974487,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10320","display_name":"Neural Networks and Applications","score":0.9316999912261963,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13382","display_name":"Robotics and Automated Systems","score":0.9243999719619751,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/computer-science","display_name":"Computer science","score":0.7299063205718994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7212319374084473},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6948313117027283},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.692579448223114},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6901611089706421},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6275293231010437},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.5551264882087708},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5527905821800232},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5411514639854431},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5261337757110596},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5109730958938599},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4819659888744354},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.4692190885543823},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44185152649879456},{"id":"https://openalex.org/keywords/viola\u2013jones-object-detection-framework","display_name":"Viola\u2013Jones object detection framework","score":0.4345196485519409},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.15770545601844788},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.07858765125274658},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07610678672790527}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7299063205718994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7212319374084473},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6948313117027283},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.692579448223114},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6901611089706421},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6275293231010437},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.5551264882087708},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5527905821800232},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5411514639854431},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5261337757110596},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5109730958938599},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4819659888744354},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.4692190885543823},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44185152649879456},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.4345196485519409},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.15770545601844788},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.07858765125274658},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07610678672790527},{"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iceic51217.2021.9369802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic51217.2021.9369802","pdf_url":null,"source":{"id":"https://openalex.org/S4306498844","display_name":"2021 International Conference on Electronics, Information, and Communication (ICEIC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Electronics, Information, and Communication (ICEIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321288","display_name":"Semyung University","ror":"https://ror.org/01d100w34"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1878852891","https://openalex.org/W1975470076","https://openalex.org/W2012557818","https://openalex.org/W2145550673","https://openalex.org/W2246284352","https://openalex.org/W2259263553","https://openalex.org/W2962882167"],"related_works":["https://openalex.org/W3160582076","https://openalex.org/W3204852000","https://openalex.org/W2965402078","https://openalex.org/W2963251476","https://openalex.org/W4297797959","https://openalex.org/W4297491714","https://openalex.org/W4256727525","https://openalex.org/W3108847140","https://openalex.org/W4200612475","https://openalex.org/W4387272257"],"abstract_inverted_index":{"In":[0,30,101],"this":[1],"paper,":[2],"we":[3],"proposed":[4,15,36,72,104],"an":[5],"efficient":[6,108],"method":[7,16,37,105],"of":[8,45,70],"recognition":[9],"for":[10,56],"object":[11,109],"detection":[12,110],"systems.":[13],"The":[14],"uses":[17],"convolution":[18],"layer":[19],"and":[20,26,50,64,97],"PCA":[21],"to":[22,43,52,83,85],"quickly":[23],"reduce":[24],"dimensions":[25],"extract":[27],"reliable":[28],"features.":[29],"addition,":[31],"features":[32],"extracted":[33],"from":[34,81],"the":[35,46,60,68,71,75,92,103],"calculate":[38],"vectors":[39],"in":[40],"areas":[41],"corresponding":[42],"background":[44],"image.":[47],"Called":[48],"eigenvalues":[49],"used":[51],"create":[53],"a":[54,90],"mask":[55],"background.":[57],"It":[58],"splits":[59],"image":[61,78],"into":[62],"objects":[63],"backgrounds.":[65],"To":[66],"determine":[67],"efficiency":[69],"method,":[73],"evaluate":[74],"INRIA":[76],"benchmark":[77],"data":[79],"set":[80],"640\u00d7480":[82],"128\u00d764":[84],"measure":[86],"accuracy":[87,96],"performance.":[88],"As":[89],"result,":[91],"performance":[93],"showed":[94],"high":[95],"low":[98],"parameter":[99],"memory.":[100],"conclusion,":[102],"confirmed":[106],"that":[107],"is":[111],"possible.":[112]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
