@inproceedings{7de56bdde12d42c7ae13cf51d1337979,
title = "A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection",
abstract = "Anomalies can be defined as any non-random structure which deviates from normality. Anomaly detection methods reported in the literature are numerous and diverse, as what is considered anomalous usually varies depending on particular scenarios and applications. In this work we propose an a contrario framework to detect anomalies in images applying statistical analysis to feature maps obtained via convolutions. We evaluate filters learned from the image under analysis via patch PCA and the feature maps obtained from a pre-trained deep neural network (Resnet). The proposed method is multi-scale and fully unsupervised, and is able to detect anomalies in a wide variety of scenarios. While the end goal of this work is the detection of subtle defects in leather samples for the automotive industry, we show that the same algorithm achieves state-of-the-art results in public anomalies datasets.",
keywords = "A contrario detection, Anomaly detection, Mahalanobis distance, Multi-scale, NFA, Number of false alarms, PCA, Principal components analysis",
author = "Matias Tailanian and Pablo Muse and Alvaro Pardo",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 ; Conference date: 13-12-2021 Through 16-12-2021",
year = "2021",
doi = "10.1109/ICMLA52953.2021.00035",
language = "Ingl{\'e}s",
series = "Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "179--184",
editor = "Wani, {M. Arif} and Sethi, {Ishwar K.} and Weisong Shi and Guangzhi Qu and Raicu, {Daniela Stan} and Ruoming Jin",
booktitle = "Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021",
}