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- W4285241348 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." @default.
- W4285241348 created "2022-07-14" @default.
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- W4285241348 date "2022-01-01" @default.
- W4285241348 modified "2023-09-26" @default.
- W4285241348 title "Deep Autoencoders for Anomaly Detection in Textured Images Using CW-SSIM" @default.
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- W4285241348 doi "https://doi.org/10.1007/978-3-031-06430-2_56" @default.
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