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Anomalies can be defined as any non-random structure which deviates from normality.
F. Cao, J. Lisani, J.-M. Morel, P. Musé, and F. Sur, A theory of shape identification , ser. Lecture Notes in Mathematics. Springer, 2008, vol. 1948
1948
Earlier work this paper cites.
A. P. Witkin and J. M. Tenenbaum, “On the role of structure in vision,” in Human and machine vision . Elsevier, 1983, pp. 481–543
1983
Earlier work this paper cites.
M. M. Moya, M. W. Koch, and L. D. Hostetler, “One-class classifier networks for target recognition applications,” NASA STI/Recon Technical Report N , vol. 93, p. 24043, 1993
1993
Earlier work this paper cites.
I. Jensen and A. J. Guttmann, “Statistics of lattice animals (polyominoes) and polygons,” Journal of Physics A: Mathematical and General , vol. 33, no. 29, p. L257, 2000
2000
Earlier work this paper cites.
A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) - Volume 2 - Volume 02 , ser. CVPR ’05. Washington, DC, USA: IEEE Computer Society, 2005, pp. 60–65
2005
Earlier work this paper cites.
A. Desolneux, L. Moisan, and J.-M. Morel, From gestalt theory to image analysis: a probabilistic approach . Springer Science & Business Media, 2007, vol. 34
2007
Earlier work this paper cites.
J. Harel, C. Koch, and P. Perona, “Graph-based visual saliency,” , 2007
2007
Earlier work this paper cites.
R. G. Von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, “Lsd: A fast line segment detector with a false detection control,” IEEE transactions on pattern analysis and machine intelligence , vol. 32, no. 4, pp. 722–732, 2008
2008
Earlier work this paper cites.
B. Du and L. Zhang, “Random-selection-based anomaly detector for hyperspectral imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 49, no. 5, pp. 1578–1589, 2010
2010
Earlier work this paper cites.
D. Lowe, Perceptual organization and visual recognition . Springer Science & Business Media, 2012, vol. 5
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. Lezama, J.-M. Morel, G. Randall, and R. G. Von Gioi, “A contrario 2d point alignment detection,” IEEE transactions on pattern analysis and machine intelligence , vol. 37, no. 3, pp. 499–512, 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Cited alongside, same era.
X. Huang, C. Shen, X. Boix, and Q. Zhao, “Salicon: Reducing the semantic gap in saliency prediction by adapting deep neural networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 262–270
2015
Cited alongside, same era.
T. Böttger and M. Ulrich, “Real-time texture error detection on textured surfaces with compressed sensing,” Pattern Recognition and Image Analysis , vol. 26, no. 1, pp. 88–94, 2016
2016
Cited alongside, same era.
T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs, “Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,” in International conference on information processing in medical imaging . Springer, 2017, pp. 146–157
2017
2020
Later among the works it cites.
2020
Later among the works it cites.
J. Yi and S. Yoon, “Patch svdd: Patch-level svdd for anomaly detection and segmentation,” in Proceedings of the Asian Conference on Computer Vision , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
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P. Napoletano, F. Piccoli, and R. Schettini, “Anomaly detection in nanofibrous materials by cnn-based self-similarity,” Sensors , vol. 18, no. 1, p. 209, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
L. Ruff, R. Vandermeulen, N. Goernitz, L. Deecke, S. A. Siddiqui, A. Binder, E. Müller, and M. Kloft, “Deep one-class classification,” in International conference on machine learning . PMLR, 2018, pp. 4393–4402
2018
Cited alongside, same era.
2018
Cited alongside, same era.
T. Ehret, A. Davy, M. Delbracio, and J.-M. Morel, “How to reduce anomaly detection in images to anomaly detection in noise,” Image Processing On Line , vol. 9, pp. 391–412, 2019
2019
Cited alongside, same era.
S. Le Hégarat-Mascle, E. Aldea, and J. Vandoni, “Efficient evaluation of the number of false alarm criterion,” EURASIP Journal on Image and Video Processing , vol. 2019, no. 1, pp. 1–15, 2019
2019
Cited alongside, same era.
P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger, “Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9592–9600
2019
Cited alongside, same era.
2020
Cited alongside, same era.
2020
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2021
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2021
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2021
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R. G. von Gioi, C. Hessel, T. Dagobert, J.-M. Morel, and C. de Franchis, “Ground visibility in satellite optical time series based on a contrario local image matching,” Image Processing On Line , vol. 11, pp. 212–233, 2021
2021
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2021
Later among the works it cites.
M. Gardella, P. Musé, J.-M. Morel, and M. Colom, “Noisesniffer: a fully automatic image forgery detector based on noise analysis,” in 2021 IEEE International Workshop on Biometrics and Forensics (IWBF) . IEEE, 2021, pp. 1–6
2021
Later among the works it cites.
H. Jiang, J. Wang, Z. Yuan, Y. Wu, N. Zheng, and S. Li, “Salient object detection: A discriminative regional feature integration approach,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2013, pp. 2083–2090
2090
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