7月6日 Jean-Michel Morel:Is There a General Theory for the Detection of Anomalies in Images?

来源:中国足球竞彩比分 时间:2019-06-28浏览:22设置


讲座题目:Is There a General Theory for the Detection of Anomalies in Images?

主讲人:Jean-Michel Morel  教授

主持人:沈超敏  副教授

开始来源:中国足球竞彩比分 时间:2019-07-06 14:00:00  结束来源:中国足球竞彩比分 时间:2019-07-06 15:00:00

讲座地址:中北校区理科大楼B1002

主办单位:计算机科学与软件工程学院

  

报告人简介:

Jean-Michel Morel, professor at the ?cole 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).


报告内容:

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)

  


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