{"id":"https://openalex.org/W3177716034","doi":"https://doi.org/10.1109/tie.2021.3094452","title":"Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature","display_name":"Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature","publication_year":2021,"publication_date":"2021-07-09","ids":{"openalex":"https://openalex.org/W3177716034","doi":"https://doi.org/10.1109/tie.2021.3094452","mag":"3177716034"},"language":"en","primary_location":{"id":"doi:10.1109/tie.2021.3094452","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2021.3094452","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Electronics","raw_type":"journal-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/A5033335952","display_name":"Qian Wan","orcid":"https://orcid.org/0000-0002-9517-7698"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Wan","raw_affiliation_strings":["State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-9517-7698","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017886286","display_name":"Liang Gao","orcid":"https://orcid.org/0000-0002-1485-0722"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Gao","raw_affiliation_strings":["State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-1485-0722","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406104","display_name":"Xinyu Li","orcid":"https://orcid.org/0000-0002-3730-0360"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Li","raw_affiliation_strings":["State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-3730-0360","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001395798","display_name":"Long Wen","orcid":"https://orcid.org/0000-0002-8355-9947"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Long Wen","raw_affiliation_strings":["School of Mechanical Engineering & Electronic Information, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-8355-9947","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering & Electronic Information, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.8893,"has_fulltext":false,"cited_by_count":88,"citation_normalized_percentile":{"value":0.97393839,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"69","issue":"6","first_page":"6182","last_page":"6192"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9933000206947327,"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/T10809","display_name":"Occupational Health and Safety Research","score":0.9194999933242798,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7934308052062988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7212691307067871},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7036946415901184},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6993178129196167},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6580014824867249},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5616551041603088},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5193782448768616},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5079846978187561},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5061013102531433},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49746230244636536},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4878499507904053},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.4101680517196655},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.26715564727783203},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2027057409286499}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7934308052062988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7212691307067871},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7036946415901184},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6993178129196167},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6580014824867249},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5616551041603088},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5193782448768616},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5079846978187561},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5061013102531433},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49746230244636536},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4878499507904053},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.4101680517196655},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.26715564727783203},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2027057409286499},{"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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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/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":1,"locations":[{"id":"doi:10.1109/tie.2021.3094452","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2021.3094452","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G7308127663","display_name":null,"funder_award_id":"51721092","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G993600603","display_name":null,"funder_award_id":"2018AAA0101704","funder_id":"https://openalex.org/F4320336026","funder_display_name":"National Key Research and Development Program of China Stem Cell and Translational Research"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336026","display_name":"National Key Research and Development Program of China Stem Cell and Translational Research","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1977556410","https://openalex.org/W2113101204","https://openalex.org/W2117539524","https://openalex.org/W2122111042","https://openalex.org/W2125027820","https://openalex.org/W2133665775","https://openalex.org/W2194775991","https://openalex.org/W2204904589","https://openalex.org/W2302255633","https://openalex.org/W2314036174","https://openalex.org/W2549139847","https://openalex.org/W2560023338","https://openalex.org/W2562062856","https://openalex.org/W2590856740","https://openalex.org/W2599354622","https://openalex.org/W2616247523","https://openalex.org/W2763583057","https://openalex.org/W2765854388","https://openalex.org/W2768753204","https://openalex.org/W2784032999","https://openalex.org/W2795739134","https://openalex.org/W2809705434","https://openalex.org/W2867167548","https://openalex.org/W2912272978","https://openalex.org/W2948334075","https://openalex.org/W2948982773","https://openalex.org/W2964137095","https://openalex.org/W2980611806","https://openalex.org/W2987228832","https://openalex.org/W2998940023","https://openalex.org/W3023868590","https://openalex.org/W3034314048","https://openalex.org/W3043445295","https://openalex.org/W3089028909","https://openalex.org/W3092704883","https://openalex.org/W3101017490","https://openalex.org/W3102564565","https://openalex.org/W3106250896","https://openalex.org/W3106848223","https://openalex.org/W3108975592","https://openalex.org/W3109715690","https://openalex.org/W3117515147","https://openalex.org/W3129166376","https://openalex.org/W3160366495","https://openalex.org/W4287869122","https://openalex.org/W4393805319","https://openalex.org/W6698183232","https://openalex.org/W6713132643","https://openalex.org/W6735108248","https://openalex.org/W6752760542","https://openalex.org/W6771530079","https://openalex.org/W6777869702","https://openalex.org/W6785652829","https://openalex.org/W6788166292"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4300558037","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"Anomaly":[0],"localization":[1,52,81,148],"is":[2,19,24,155],"valuable":[3],"for":[4,37,45,79,146,188,206],"improvement":[5],"of":[6,17,51,69,88,114,138,169],"complex":[7],"production":[8],"processing":[9],"in":[10,49,82],"smart":[11],"manufacturing":[12],"system.":[13],"As":[14],"the":[15,74,110,123,130,134,150,158,185,195],"distribution":[16],"anomalies":[18],"unknowable":[20],"and":[21,56,76,104,126,143,175,179,192,199],"labeled":[22],"data":[23],"few,":[25],"unsupervised":[26,83,189],"methods":[27,160,187],"based":[28],"on":[29,161],"convolutional":[30],"neural":[31],"network":[32],"(CNN)":[33],"have":[34],"been":[35],"studied":[36],"anomaly":[38,80,106,147,190],"localization.":[39],"But":[40],"there":[41],"are":[42,117,141,204],"still":[43],"problems":[44],"real":[46,207],"industrial":[47,208],"applications,":[48],"terms":[50],"accuracy,":[53],"computation":[54,197],"time,":[55],"memory":[57,201],"storage.":[58],"This":[59],"article":[60],"proposes":[61],"a":[62],"novel":[63],"framework":[64],"called":[65],"as":[66],"Gaussian":[67,101],"clustering":[68,75,102,111],"pretrained":[70,93,120],"feature":[71,95],"(GCPF),":[72],"including":[73],"inference":[77,135],"stage,":[78,112,136],"way.":[84],"The":[85,153,182],"GCPF":[86,154,183],"consists":[87],"three":[89],"modules":[90],"which":[91,203],"include":[92],"deep":[94],"extraction":[96],"(PDFE),":[97],"multiple":[98],"independent":[99],"multivariate":[100],"(MIMGC),":[103],"multihierarchical":[105],"scoring":[107],"(MHAS).":[108],"In":[109,133],"features":[113,137],"normal":[115],"images":[116,140],"extracted":[118,142],"by":[119],"CNN":[121],"at":[122,129,149],"PDFE":[124],"module,":[125],"then":[127,144],"clustered":[128],"MIMGC":[131],"module.":[132,152],"target":[139],"scored":[145],"MHAS":[151],"compared":[156,186],"with":[157],"state-of-the-art":[159],"MVTec":[162],"dataset,":[163],"achieving":[164],"receiver":[165],"operating":[166],"characteristic":[167],"curve":[168],"96.86%":[170],"over":[171],"all":[172],"15":[173],"categories,":[174],"extended":[176],"to":[177],"NanoTWICE":[178],"DAGM":[180],"datasets.":[181],"outperforms":[184],"localization,":[191],"significantly":[193],"reserves":[194],"low":[196],"complexity":[198],"online":[200],"storage":[202],"important":[205],"applications.":[209]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
