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Is There a General Theory for the Detection of Anomalies in Images?

发布日期: 2019-07-06   浏览次数 16

报告题目:Is There a General Theory for the Detection of Anomalies in Images?

报告人:Jean-Michel Morel 教授  法国加香高师

主持人:沈超敏 副教授

报告时间:2019年7月6日  周六14:00-15:00 

报告地点:理科大楼B1002


报告摘要:

Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image. Detection methods have been proposed by the thousands because each problem requires a different background model. By analysing key examples of the literature, we show that all anomaly detectors are characterized by their choice among seven fundamental principles guiding the background model and the decision method. We show that these principles can be combined in a general method that uses six of them. Our synthesis reduces the problem to the easier problem of detecting anomalies in noise. In that way, the varifold background modeling problem is replaced by simpler noise modeling, and allows the calculation of rigorous thresholds based on the a contrario detection theory. Our conclusion is that it is possible to perform automatic anomaly detection even on a single image. (Joint work of Thibaud Ehret, Axel Davy, Jean-Michel Morel, Mauricio Delbracio)


报告人简介:

Jean-Michel Morel, professor at the École normale supérieure of Cachan. Mathematics Center and their applications, lauréat of the Grand Prix Inria - Academy of Sciences in 2013. Focusing on the analysis and mathematical processing of images, Prof Morel’s most notable contributions are in the areas of segmentation, denoising, mapping, and detecting significant events in digital images. Prof. Morel is the founder of the online scientific publication “Image Processing OnLine” (http://www.ipol.im/). He has won numerous prizes, including Philip Morris Mathematics Prize (1991), CISI-Engineering Award for Applied Mathematics (1992),Science and Defense Award (1996), INRIA Grand Prix - Academy of Sciences (2013), CNRS Medal of Innovation (2015) and IEEE CVPR Longuet-Higgins Prize (2015).