A Benchmark of Traditional Visual Descriptors and Convolutional Networks ‘Off-the-Shelf’ for Anomaly Detection

Author:

Bianconi FrancescoORCID,Conti Paolo,Zanetti Elisabetta MariaORCID,Pascoletti GiuliaORCID

Publisher

Springer International Publishing

Reference30 articles.

1. Arun Pandian, R.: Surface crack detection: concrete surface sample images for surface crack detection. https://www.kaggle.com/arunrk7/surface-crack-detection. (Accessed 3 Nov 2021)

2. Bello-Cerezo, R., Bianconi, F., Di Maria, F., Napoletano, P., Smeraldi, F.: Comparative evaluation of hand-crafted image descriptors vs. off-the-shelf CNN-based features for colour texture classification under ideal and realistic conditions. Appli. Sci. 9(4), 738 (2019)

3. Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., Steger, C.: The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection. Int. J. Comput. Vision 129(4), 1038–1059 (2021)

4. Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD-A comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, United States, pp. 9584–9592. IEEE Computer Society, Jun 2021

5. Bianconi, F., Bello-Cerezo, R., Napoletano, P.: Improved opponent color local binary patterns: an effective local image descriptor for color texture classification. J. Electron. Imaging 27(1), 011002 (2018)

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