Abstract
Detecting anomalous regions in images is a frequently encountered problem in industrial monitoring. A relevant example is the analysis of tissues and other products that in normal conditions conform to a specific texture, while defects introduce changes in the normal pattern. We address the anomaly detection problem by training a deep autoencoder, and we show that adopting a loss function based on Complex Wavelet Structural Similarity (CW-SSIM) yields superior detection performance on this type of images compared to traditional autoencoder loss functions. Our experiments on well-known anomaly detection benchmarks show that a simple model trained with this loss function can achieve comparable or superior performance to state-of-the-art methods leveraging deeper, larger and more computationally demanding neural networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622–637. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_39
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. Vis. 129(4), 1038–1059 (2021)
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4183–4192 (2020)
Bergmann., P., Löwe., S., Fauser., M., Sattlegger., D., Steger., C.: Improving unsupervised defect segmentation by applying structural similarity to autoencoders. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), pp. 372–380. SciTePress (2019)
Bölgen, N., Vaseashta, A.: Nanofibers for tissue engineering and regenerative medicine. In: Sontea, V., Tiginyanu, I. (eds.) 3rd International Conference on Nanotechnologies and Biomedical Engineering. IP, vol. 55, pp. 319–322. Springer, Singapore (2016). https://doi.org/10.1007/978-981-287-736-9_77
Carrera, D., Manganini, F., Boracchi, G., Lanzarone, E.: Defect detection in SEM images of nanofibrous materials. IEEE Trans. Ind. Inf. 13(2), 551–561 (2016)
Chetverikov, D., Hanbury, A.: Finding defects in texture using regularity and local orientation. Pattern Recogn. 35(10), 2165–2180 (2002)
Freeman, W.T., Adelson, E.H., et al.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13, 891–906 (1991)
Haindl, M., Grim, J., Mikeš, S.: Texture defect detection. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 987–994. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74272-2_122
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)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (2015)
Li, K.L., Huang, H.K., Tian, S.F., Xu, W.: Improving one-class SVM for anomaly detection. In: Proceedings of the 2003 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 3077–3081. IEEE (2003)
Mojsilovic, A., Hu, H., Soljanin, E.: Extraction of perceptually important colors and similarity measurement for image matching, retrieval and analysis. IEEE Trans. Image Process. 11(11), 1238–1248 (2002)
Napoletano, P., Piccoli, F., Schettini, R.: Anomaly detection in nanofibrous materials by CNN-based self-similarity. Sensors 18(1), 209 (2018)
Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33, 1065–1076 (1962)
Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. Int. J. Comput. Vis. 40, 49–70 (2000)
Ruff, L., Kauffmann, J.R., Vandermeulen, R.A., Montavon, G., Samek, W., Kloft, M., Dietterich, T.G., Müller, K.R.: A unifying review of deep and shallow anomaly detection. Proc. IEEE 109(5), 756–795 (2021)
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)
Simoncelli, E.P., Freeman, W.T., Adelson, E.H., Heeger, D.J.: Shiftable multiscale transforms. IEEE Trans. Inf. Theory 38, 587–607 (1992)
Tsa, D.M., Wu, S.K.: Automated surface inspection using Gabor filters. Int. J. Adv. Manuf. Technol. 16(7), 474–482 (2000)
Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398–1402 (2003)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wang, Z., Simoncelli, E.P.: Translation insensitive image similarity in complex wavelet domain. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2005. vol. 2, pp. 573–576. IEEE (2005)
Zavrtanik, V., Kristan, M., Skočaj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 112, 107706 (2021)
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)
Zujovic, J., Pappas, T.N., Neuhoff, D.L.: Structural similarity metrics for texture analysis and retrieval. In: IEEE International Conference on Image Processing (ICIP), pp. 2225–2228. IEEE (2009)
Acknowledgement
We gratefully acknowledge the support of NVIDIA Corporation with the four RTX A6000 GPUs granted through the Applied Research Accelerator Program to Politecnico di Milano.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bionda, A., Frittoli, L., Boracchi, G. (2022). Deep Autoencoders for Anomaly Detection in Textured Images Using CW-SSIM. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_56
Download citation
DOI: https://doi.org/10.1007/978-3-031-06430-2_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06429-6
Online ISBN: 978-3-031-06430-2
eBook Packages: Computer ScienceComputer Science (R0)