{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T22:14:02Z","timestamp":1770416042018,"version":"3.49.0"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) Grant","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korean Government","award":["2020R1C1C1012479"],"award-info":[{"award-number":["2020R1C1C1012479"]}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT (MSIT), South Korea, through the Grand Information & Communication Technology Research Center Support Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute for Information & Communications Technology Planning & Evaluation","award":["IITP-2020-0-101741"],"award-info":[{"award-number":["IITP-2020-0-101741"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3041790","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T22:35:02Z","timestamp":1606862102000},"page":"216781-216792","source":"Crossref","is-referenced-by-count":9,"title":["Spatially Variant Convolutional Autoencoder Based on Patch Division for Pill Defect Detection"],"prefix":"10.1109","volume":"8","author":[{"given":"Sora","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Hanyang University, Ansan, South Korea"}]},{"given":"Youngjae","family":"Jo","sequence":"additional","affiliation":[{"name":"ADAS Platform Cell Team, Hyundai Mobis Company Ltd., Yongin, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3859-1702","authenticated-orcid":false,"given":"Jungchan","family":"Cho","sequence":"additional","affiliation":[{"name":"College of Information Technology, Gachon University, Seongnam, South Korea"}]},{"given":"Jiwoo","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Hanyang University, Ansan, South Korea"}]},{"given":"Younyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Advanced Technology Center, Daekhon Corporation, Seoul, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4941-4311","authenticated-orcid":false,"given":"Minsik","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Hanyang University, Ansan, South Korea"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.01.010"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2670780"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2018.02.006"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2900907"},{"key":"ref36","article-title":"Efficient GAN-based anomaly detection","author":"zenati","year":"2018","journal-title":"arXiv 1802 06222"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2015.06.451"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3390\/app8091575"},{"key":"ref10","article-title":"Adversarial feature learning","author":"donahue","year":"2016","journal-title":"arXiv 1605 09782"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2930682"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00179"},{"key":"ref13","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1989.tb01764.x"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.2174\/1874444301406010940"},{"key":"ref16","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2013","journal-title":"arXiv 1312 6114"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2014.2358029"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2959741"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/s18041064"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00356"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"‘GrabCut’: Interactive foreground extraction using iterated graph cuts","volume":"23","author":"rother","year":"2004","journal-title":"ACM Trans Graph"},{"key":"ref3","article-title":"Wasserstein GAN","author":"arjovsky","year":"2017","journal-title":"arXiv 1701 07875"},{"key":"ref6","first-page":"105","article-title":"Interactive graph cuts for optimal boundary & region segmentation of objects in ND images","volume":"1","author":"boykov","year":"2001","journal-title":"Proc 8th IEEE Int Conf Comput Vis (ICCV)"},{"key":"ref29","first-page":"488","article-title":"AVID: Adversarial visual irregularity detection","author":"sabokrou","year":"2018","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/130385.130401"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2005.151"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"ref2","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"an","year":"2015","journal-title":"Proc Special Lect IE"},{"key":"ref1","first-page":"622","article-title":"Ganomaly: Semi-supervised anomaly detection via adversarial training","author":"akcay","year":"2018","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1684(94)90060-4"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/1049-9652(92)90034-U"},{"key":"ref21","first-page":"1","article-title":"Principal component analysis in image processing","author":"mudrova","year":"2005","journal-title":"Proc MATLAB Tech Comput Conf"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01219"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2882070"},{"key":"ref25","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09274349.pdf?arnumber=9274349","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T20:18:54Z","timestamp":1662668334000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9274349\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3041790","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}