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In this work we propose a one-class self-supervised method for anomaly segmentation in images that benefits both from a modern machine learning approach and a more classic statistical detection theory.
Chernoff, H.: A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. The Annals of Mathematical Statistics, 493–507 (1952)
1952
Earlier work this paper cites.
Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, Inc., USA (1983)
1983
Earlier work this paper cites.
Lowe, D.G.: Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, USA (1985)
1985
Earlier work this paper cites.
Monasse, P., Guichard, F.: Fast computation of a contrast-invariant image representation. IEEE Transactions on Image Processing 9
2000
Earlier work this paper cites.
Jensen, I., Guttmann, A.J.: Statistics of lattice animals (polyominoes) and polygons. Journal of Physics A: Mathematical and General 33
2000
Earlier work this paper cites.
Desolneux, A., Moisan, L., Morel, J.-M.: Edge detection by Helmholtz principle. Journal of mathematical imaging and vision 14
2001
Earlier work this paper cites.
Ballester, C., Caselles, V., Monasse, P.: The tree of shapes of an image. ESAIM: Control, Optimisation and Calculus of Variations 9
2003
Earlier work this paper cites.
Cao, F., Musé, P., Sur, F.: Extracting meaningful curves from images. Journal of Mathematical Imaging and Vision 22
2005
Earlier work this paper cites.
Musé, P., Sur, F., Cao, F., Gousseau, Y., Morel, J.-M.: An a contrario decision method for shape element recognition. International Journal of Computer Vision 69
2006
Earlier work this paper cites.
Cao, F., Delon, J., Desolneux, A., Musé, P., Sur, F.: A unified framework for detecting groups and application to shape recognition. Journal of Mathematical Imaging and Vision 27
2007
Earlier work this paper cites.
Desolneux, A., Moisan, L., Morel, J.-M.: From Gestalt theory to image analysis: A probabilistic approach. Interdisciplinary Applied Mathematics ( (2008)
2008
Earlier work this paper cites.
Von Gioi, R.G., Jakubowicz, J., Morel, J.-M., Randall, G.: On straight line segment detection. Journal of Mathematical Imaging and Vision 32
2008
Earlier work this paper cites.
Grosjean, B., Moisan, L.: A-contrario detectability of spots in textured backgrounds. Journal of Mathematical Imaging and Vision 33
2009
Earlier work this paper cites.
Xu, Y., Géraud, T., Najman, L.: Context-based energy estimator: Application to object segmentation on the tree of shapes. In: 2012 19th IEEE International Conference on Image Processing, pp. 1577–1580 (2012)
2012
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: 2nd International Conference on Learning Representations (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer
2015
Earlier work this paper cites.
Rezende, D., Mohamed, S.: Variational inference with normalizing flows. In: International Conference on Machine Learning, pp. 1530–1538 (2015)
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
2016
Earlier work this paper cites.
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Intl. Conf. on Information Processing in Medical Imaging, pp. 146–157 (2017). Springer
2017
Cited alongside, same era.
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 665–674 (2017)
2017
Cited alongside, same era.
Kingma, D.P., Dhariwal, P.: Glow: Generative flow with invertible 1x1 convolutions. Advances in neural information processing systems 31
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2021
Later among the works it cites.
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., Jégou, H.: Going deeper with image transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 32–42 (2021)
2021
Later among the works it cites.
Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. In: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), pp. 01–06 (2021)
2021
Later among the works it cites.
Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: International Conference on Pattern Recognition, pp. 475–489 (2021). Springer
2021
Later among the works it cites.
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Liu, W., W. Luo, D.L., Gao, S.: Future frame prediction for anomaly detection – a new baseline. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Davy, A., Ehret, T., Morel, J.-M., Delbracio, M.: Reducing anomaly detection in images to detection in noise. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1058–1062 (2018). IEEE
2018
Cited alongside, same era.
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.v.d.: Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1705–1714 (2019)
2019
Cited alongside, same era.
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical image analysis 54
2019
Cited alongside, same era.
Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: Ganomaly: Semi-supervised anomaly detection via adversarial training. In: Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III 14, pp. 622–637 (2019). Springer
2019
Cited alongside, same era.
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9592–9600 (2019)
2019
Cited alongside, same era.
Buda, M., Saha, A., Mazurowski, M.A.: Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Computers in biology and medicine 109
2019
Cited alongside, same era.
Li, C.-L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: Self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9664–9674 (2021)
2021
Later among the works it cites.
Rudolph, M., Wandt, B., Rosenhahn, B.: Same same but differnet: Semi-supervised defect detection with normalizing flows. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1907–1916 (2021)
2021
Later among the works it cites.
Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., Feichtenhofer, C.: Multiscale vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6824–6835 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Gardella, M., Musé, P., Morel, J.-M., Colom, M.: Noisesniffer: a fully automatic image forgery detector based on noise analysis. In: 2021 IEEE International Workshop on Biometrics and Forensics (IWBF), pp. 1–6 (2021). IEEE
2021
Later among the works it cites.
Gioi, R.G., Hessel, C., Dagobert, T., Morel, J.-M., Franchis, C.: Ground visibility in satellite optical time series based on a contrario local image matching. Image Processing On Line 11
2021
Later among the works it cites.
Zheng, Y., Wang, X., Deng, R., Bao, T., Zhao, R., Wu, L.: Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization. In: 2022 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2022). IEEE
2022
Closest in time.
2022
Closest in time.
Rudolph, M., Wehrbein, T., Rosenhahn, B., Wandt, B.: Fully convolutional cross-scale-flows for image-based defect detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1088–1097 (2022)
2022
Closest in time.
Akcay, S., Ameln, D., Vaidya, A., Lakshmanan, B., Ahuja, N., Genc, U.: Anomalib: A Deep Learning Library for Anomaly Detection (2022)
2022
Closest in time.
Tsai, C.-C., Wu, T.-H., Lai, S.-H.: Multi-scale patch-based representation learning for image anomaly detection and segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3992–4000 (2022)
2022
Closest in time.
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318–14328 (2022)
2022
Closest in time.
2022
Closest in time.
Gudovskiy, D., Ishizaka, S., Kozuka, K.: Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 98–107 (2022)
2022
Closest in time.
2022
Closest in time.
Wan, Q., Cao, Y., Gao, L., Shen, W., Li, X.: Position encoding enhanced feature mapping for image anomaly detection. In: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), pp. 876–881 (2022)
2022
Closest in time.
Ardizzone, L., Bungert, T., Draxler, F., Köthe, U., Kruse, J., Schmier, R., Sorrenson, P.: Framework for Easily Invertible Architectures (FrEIA) (2018-2022). https://github.com/vislearn/FrEIA
2022
Closest in time.