{"id":"https://openalex.org/W4413278361","doi":"https://doi.org/10.1109/icip55913.2025.11084350","title":"Measuring Distortion Strength with Dewarping Diffusion Models in Anomaly Detection","display_name":"Measuring Distortion Strength with Dewarping Diffusion Models in Anomaly Detection","publication_year":2025,"publication_date":"2025-08-18","ids":{"openalex":"https://openalex.org/W4413278361","doi":"https://doi.org/10.1109/icip55913.2025.11084350"},"language":"en","primary_location":{"id":"doi:10.1109/icip55913.2025.11084350","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip55913.2025.11084350","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Image Processing (ICIP)","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/A5119330224","display_name":"Akira Uchida","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akira Uchida","raw_affiliation_strings":["Institute of Science Tokyo,Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Science Tokyo,Tokyo,Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069356829","display_name":"Satoshi Ikehata","orcid":"https://orcid.org/0000-0002-6061-7956"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Satoshi Ikehata","raw_affiliation_strings":["Institute of Science Tokyo,Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Science Tokyo,Tokyo,Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038701345","display_name":"Yuichi Yoshida","orcid":"https://orcid.org/0000-0001-8919-8479"},"institutions":[{"id":"https://openalex.org/I4210132650","display_name":"Denso (Japan)","ror":"https://ror.org/04hkpfa76","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210132650"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuichi Yoshida","raw_affiliation_strings":["Denso IT Laboratory,Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Denso IT Laboratory,Tokyo,Japan","institution_ids":["https://openalex.org/I4210132650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069458206","display_name":"Ikuro Sato","orcid":"https://orcid.org/0000-0001-5234-3177"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ikuro Sato","raw_affiliation_strings":["Institute of Science Tokyo,Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Science Tokyo,Tokyo,Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16979375,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2157","last_page":"2162"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9179999828338623,"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":0.9179999828338623,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.6091525554656982},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5856689214706421},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5794712901115417},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.5562399625778198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4139788746833801},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2945561110973358},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12186074256896973},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10651096701622009},{"id":"https://openalex.org/keywords/condensed-matter-physics","display_name":"Condensed matter physics","score":0.077686607837677},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.062090545892715454}],"concepts":[{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.6091525554656982},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5856689214706421},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5794712901115417},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.5562399625778198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4139788746833801},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2945561110973358},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12186074256896973},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10651096701622009},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.077686607837677},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.062090545892715454},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip55913.2025.11084350","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip55913.2025.11084350","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320317941","display_name":"DENSO","ror":"https://ror.org/04hkpfa76"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1966832848","https://openalex.org/W2336763592","https://openalex.org/W2599354622","https://openalex.org/W2948982773","https://openalex.org/W3092704883","https://openalex.org/W3109771882","https://openalex.org/W3147184966","https://openalex.org/W3162605107","https://openalex.org/W3169651898","https://openalex.org/W4214694907","https://openalex.org/W4292851291","https://openalex.org/W4312772600","https://openalex.org/W4376626035","https://openalex.org/W4390872231","https://openalex.org/W4390872580","https://openalex.org/W4401024429","https://openalex.org/W4402915921","https://openalex.org/W4402961692","https://openalex.org/W4409653247","https://openalex.org/W4410394489"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W3120251014"],"abstract_inverted_index":{"Surface":[0],"anomaly":[1],"detection":[2],"is":[3,22,57,142],"a":[4,11,71,93,104,110],"task":[5],"to":[6,59],"localize":[7],"abnormal":[8],"regions":[9,69],"in":[10,130],"given":[12],"image,":[13],"typically":[14],"used":[15],"for":[16,48],"product":[17],"inspection.":[18],"A":[19],"representative":[20],"approach":[21,102],"the":[23,120,136],"reconstruction-based":[24],"method,":[25],"which":[26],"detects":[27],"defects":[28],"using":[29],"reconstruction":[30],"errors":[31],"computed":[32],"by":[33],"generative":[34],"models,":[35],"such":[36],"as":[37,76,109],"diffusion":[38,112],"models":[39],"trained":[40],"exclusively":[41],"on":[42,63,115,119],"normal":[43],"images.":[44],"In":[45],"reality,":[46],"however,":[47],"products":[49],"largely":[50],"comprised":[51],"of":[52,139],"metal":[53],"or":[54],"resin,":[55],"it":[56],"common":[58],"identify":[60],"abnormalities":[61],"based":[62,114],"local":[64,99,133],"physical":[65],"distortion":[66],"levels;":[67],"i.e.,":[68],"exceeding":[70],"predefined":[72],"tolerance":[73],"are":[74,135],"regarded":[75],"defective.":[77],"Reconstruction":[78],"error-based":[79],"approaches":[80],"cannot":[81],"directly":[82,97],"estimate":[83],"this":[84,88],"metric.":[85],"To":[86],"address":[87],"issue,":[89],"we":[90],"propose":[91],"DiffuDewarp,":[92],"novel":[94],"method":[95,126],"that":[96,124],"estimates":[98],"distortions.":[100],"Our":[101],"defines":[103],"pseudo-deformation":[105],"defect":[106],"generation":[107],"process":[108,113],"new":[111],"localized":[116],"warping.":[117],"Experiments":[118],"MVTec":[121],"dataset":[122],"demonstrate":[123],"our":[125],"outperforms":[127],"state-of-the-art":[128],"techniques":[129],"categories":[131],"where":[132],"deformations":[134],"primary":[137],"cause":[138],"defects.":[140],"Code":[141],"released":[143],"at":[144],"https://github.com/UCHIDA-AKIRA018/DiffuDewarp.":[145]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}