How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise
Thibaud Ehret, Axel Davy, Mauricio Delbracio, Jean-Michel Morel
→ BibTeX
@article{ipol.2019.263,
    title   = {{How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise}},
    author  = {Ehret, Thibaud and Davy, Axel and Delbracio, Mauricio and Morel, Jean-Michel},
    journal = {{Image Processing On Line}},
    volume  = {9},
    pages   = {391--412},
    year    = {2019},
    doi     = {10.5201/ipol.2019.263},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2019.263}}
published
2019-12-08
reference
Thibaud Ehret, Axel Davy, Mauricio Delbracio, and Jean-Michel Morel, How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise, Image Processing On Line, 9 (2019), pp. 391–412. https://doi.org/10.5201/ipol.2019.263

Communicated by Yann Gousseau
Demo edited by Thibaud Ehret

Abstract

Anomaly detectors address the difficult problem of detecting automatically exceptions in a background image, that can be as diverse as a fabric or a mammography. Detection methods have been proposed by the thousands because each problem requires a different background model. By analyzing the existing approaches, we show that the problem can be reduced to detecting anomalies in residual images (extracted from the target image) in which noise and anomalies prevail. Hence, the general and impossible background modeling problem is replaced by a simple noise model, and allows the calculation of rigorous detection thresholds. Our approach is therefore unsupervised and works on arbitrary images. The residual images can favorably be computed on dense features of neural networks. Our detector is powered by the a contrario detection theory, which avoids over-detection by fixing detection thresholds taking into account the multiple tests.

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