{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:28:13Z","timestamp":1740461293068,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>Anomaly detection is an important problem with many applications in industry. This paper introduces a new methodology for detecting anomalies in a real laser heating surface process recorded with a high-speed thermal camera (1000 fps, 32&amp;times;32 pixels). The system is trained with non-anomalous data only (32 videos with 21500 frames). The proposed method is built upon kernel density estimation and is capable of detecting anomalies in time-series data. The classification should be completed in-process, that is, within the cycle time of the workpiece.<\/jats:p>","DOI":"10.3233\/978-1-61499-682-8-137","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T11:58:24Z","timestamp":1740398304000},"source":"Crossref","is-referenced-by-count":0,"title":["Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot"],"prefix":"10.3233","author":[{"family":"Atienza David","sequence":"additional","affiliation":[]},{"family":"Bielza Concha","sequence":"additional","affiliation":[]},{"family":"Diaz Javier","sequence":"additional","affiliation":[]},{"family":"Larra&ntilde;aga Pedro","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","STAIRS 2016"],"original-title":[],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T12:03:20Z","timestamp":1740398600000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-681-1&spage=137&doi=10.3233\/978-1-61499-682-8-137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-682-8-137","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2016]]}}}