{"id":"https://openalex.org/W2903934855","doi":"https://doi.org/10.23919/spa.2018.8563423","title":"Object Detection utilizing Modified Auto Encoder and Convolutional Neural Networks","display_name":"Object Detection utilizing Modified Auto Encoder and Convolutional Neural Networks","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2903934855","doi":"https://doi.org/10.23919/spa.2018.8563423","mag":"2903934855"},"language":"en","primary_location":{"id":"doi:10.23919/spa.2018.8563423","is_oa":false,"landing_page_url":"https://doi.org/10.23919/spa.2018.8563423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","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/A5010935335","display_name":"Jalil Nourmohammadi Khiarak","orcid":"https://orcid.org/0000-0002-1928-9081"},"institutions":[{"id":"https://openalex.org/I41832843","display_name":"University of Tabriz","ror":"https://ror.org/01papkj44","country_code":"IR","type":"education","lineage":["https://openalex.org/I41832843"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Jalil Nourmohammadi-Khiarak","raw_affiliation_strings":["Faculty Of Electrical And Computer Engineering, University of Tabriz, Tabriz, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty Of Electrical And Computer Engineering, University of Tabriz, Tabriz, Iran","institution_ids":["https://openalex.org/I41832843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088706141","display_name":"Samaneh Mazaheri","orcid":"https://orcid.org/0000-0003-2996-6215"},"institutions":[{"id":"https://openalex.org/I130343225","display_name":"Universiti Putra Malaysia","ror":"https://ror.org/02e91jd64","country_code":"MY","type":"education","lineage":["https://openalex.org/I130343225"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Samaneh Mazaheri","raw_affiliation_strings":["Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Selangor, Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Selangor, Malaysia","institution_ids":["https://openalex.org/I130343225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050199849","display_name":"Rohollah Moosavi-Tayebi","orcid":null},"institutions":[{"id":"https://openalex.org/I130343225","display_name":"Universiti Putra Malaysia","ror":"https://ror.org/02e91jd64","country_code":"MY","type":"education","lineage":["https://openalex.org/I130343225"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Rohollah Moosavi-Tayebi","raw_affiliation_strings":["Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Selangor, Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Selangor, Malaysia","institution_ids":["https://openalex.org/I130343225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000234515","display_name":"Noorbakhsh-Devlagh Hamid","orcid":null},"institutions":[{"id":"https://openalex.org/I39268498","display_name":"University of Isfahan","ror":"https://ror.org/05h9t7759","country_code":"IR","type":"education","lineage":["https://openalex.org/I39268498"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Hamid Noorbakhsh-Devlagh","raw_affiliation_strings":["Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran","institution_ids":["https://openalex.org/I39268498"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.318,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64200018,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"43","last_page":"49"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9995999932289124,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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":0.802155077457428},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7830802202224731},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6853018999099731},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6654534339904785},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6624583005905151},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6216319799423218},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6201425194740295},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5917824506759644},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5875881910324097},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5510042309761047},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.54375159740448},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4957064390182495},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4917202293872833},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.48014968633651733},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4134015440940857}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.802155077457428},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7830802202224731},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6853018999099731},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6654534339904785},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6624583005905151},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6216319799423218},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6201425194740295},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5917824506759644},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5875881910324097},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5510042309761047},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.54375159740448},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4957064390182495},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4917202293872833},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.48014968633651733},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4134015440940857},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/spa.2018.8563423","is_oa":false,"landing_page_url":"https://doi.org/10.23919/spa.2018.8563423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","raw_type":"proceedings-article"},{"id":"pmh:oai:psasir.upm.edu.my:69656","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196260","display_name":"Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I130343225","host_organization_name":"Universiti Putra Malaysia","host_organization_lineage":["https://openalex.org/I130343225"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7200000286102295,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W161114242","https://openalex.org/W304081852","https://openalex.org/W639708223","https://openalex.org/W1486050478","https://openalex.org/W1536680647","https://openalex.org/W1573546770","https://openalex.org/W1616516642","https://openalex.org/W1931967218","https://openalex.org/W1932624639","https://openalex.org/W1987848744","https://openalex.org/W2008676310","https://openalex.org/W2018744461","https://openalex.org/W2027090038","https://openalex.org/W2034128706","https://openalex.org/W2035903867","https://openalex.org/W2061773916","https://openalex.org/W2078635583","https://openalex.org/W2083870150","https://openalex.org/W2092183101","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2107789863","https://openalex.org/W2120284346","https://openalex.org/W2121091546","https://openalex.org/W2130306094","https://openalex.org/W2138302688","https://openalex.org/W2148958980","https://openalex.org/W2152724437","https://openalex.org/W2161969291","https://openalex.org/W2168356304","https://openalex.org/W2542685616","https://openalex.org/W2543696449","https://openalex.org/W2546280627","https://openalex.org/W2585724928","https://openalex.org/W2586392958","https://openalex.org/W2588454508","https://openalex.org/W2588525379","https://openalex.org/W2592877961","https://openalex.org/W2594268324","https://openalex.org/W2598112812","https://openalex.org/W2598559324","https://openalex.org/W2613718673","https://openalex.org/W6606595081","https://openalex.org/W6620707391","https://openalex.org/W6676071220","https://openalex.org/W6678298392","https://openalex.org/W6679349572","https://openalex.org/W6683411478"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4220682630","https://openalex.org/W3181622257","https://openalex.org/W4389832810","https://openalex.org/W3133533225"],"abstract_inverted_index":{"Deep":[0],"learning":[1,80,120,156],"models":[2,157],"are":[3,68,91,129],"widely":[4],"used":[5],"in":[6,38,56,63,137,178,180],"object":[7,36,53,82,86],"detection":[8,37,54,174],"area,":[9],"including":[10],"combination":[11],"of":[12,33,81,164,172],"multiple":[13],"non-linear":[14],"data":[15],"transformations.":[16],"The":[17,109],"objective":[18],"is":[19,61,143,149],"receiving":[20],"brief":[21],"and":[22,78,98,145,161],"concise":[23],"information":[24,148,163],"for":[25,75,85],"feature":[26,100,119,155,173],"representations.":[27],"Due":[28],"to":[29,70,93],"the":[30,52,76,138,141,152,165,183],"high":[31],"volume":[32],"processing":[34],"data,":[35],"videos":[39],"has":[40,112],"been":[41],"faced":[42],"with":[43,102,131,182],"big":[44],"challenges,":[45],"such":[46],"as":[47,123],"mass":[48],"calculation.":[49],"To":[50],"increase":[51],"precision":[55,171],"videos,":[57],"a":[58,94,132],"hybrid":[59],"method":[60,111],"proposed,":[62],"this":[64],"paper.":[65],"Some":[66],"modifications":[67],"applied":[69],"auto":[71],"encoder":[72],"neural":[73,96],"networks,":[74],"compact":[77,144],"discriminative":[79],"features.":[83,166],"Furthermore,":[84],"classification,":[87],"firstly":[88],"extracted":[89],"features":[90,128],"transferred":[92],"convolutional":[95],"network,":[97],"after":[99],"convolution":[101],"input":[103],"pictures,":[104],"they":[105],"will":[106,125],"be":[107,126],"classified.":[108],"proposed":[110,139],"two":[113],"main":[114],"advantages":[115],"over":[116],"other":[117],"unsupervised":[118,154],"techniques.":[121],"Firstly,":[122],"it":[124],"shown,":[127],"detected":[130],"much":[133],"higher":[134],"precision.":[135],"Secondly,":[136],"method,":[140],"outcome":[142],"additional":[146],"unnecessary":[147],"removed;":[150],"while":[151],"existing":[153],"mainly":[158],"learn":[159],"repeated":[160],"redundant":[162],"Experimental":[167],"evaluation":[168],"shows":[169],"that":[170],"improved":[175],"by":[176],"1.5%":[177],"average":[179],"compare":[181],"state-of-the-art":[184],"methods.":[185]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
