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pro vyhledávání: '"Bertoldo, João P. C."'
Recent advances in visual anomaly detection research have seen AUROC and AUPRO scores on public benchmark datasets such as MVTec and VisA converge towards perfect recall, giving the impression that these benchmarks are near-solved. However, high AURO
Externí odkaz:
http://arxiv.org/abs/2401.01984
Autor:
Gula, Tetiana, Bertoldo, João P C
Publikováno v:
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 4110-4118
Anomaly detection (AD) in images, identifying significant deviations from normality, is a critical issue in computer vision. This paper introduces a novel approach to dimensionality reduction for AD using pre-trained convolutional neural network (CNN
Externí odkaz:
http://arxiv.org/abs/2308.04944
Publikováno v:
2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2023
Anomaly detection (AD) in images is a fundamental computer vision problem by deep learning neural network to identify images deviating significantly from normality. The deep features extracted from pretrained models have been proved to be essential f
Externí odkaz:
http://arxiv.org/abs/2307.11197
Autor:
Bertoldo, Joao P C, Arrustico, David
Publikováno v:
2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2023, pp. 1-6
This paper introduces a simplified variation of the PaDiM (Pixel-Wise Anomaly Detection through Instance Modeling) method for anomaly detection in images, fitting a single multivariate Gaussian (MVG) distribution to the feature vectors extracted from
Externí odkaz:
http://arxiv.org/abs/2307.06052
We propose an incremental improvement to Fully Convolutional Data Description (FCDD), an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a.k.a. anomaly localization). We analyze its original l
Externí odkaz:
http://arxiv.org/abs/2301.09602
Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods. The authors claim that FCDD achieves re
Externí odkaz:
http://arxiv.org/abs/2206.02598
Publikováno v:
Front. Mater., 25 November 2021 Sec. Computational Materials Science
X-ray Computed Tomography (XCT) techniques have evolved to a point that high-resolution data can be acquired so fast that classic segmentation methods are prohibitively cumbersome, demanding automated data pipelines capable of dealing with non-trivia
Externí odkaz:
http://arxiv.org/abs/2107.07468
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