24 citations · 29 across the 6 of their papers we have counts for
7 papers
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance
Joao P. C. Bertoldo, Dick Ameln, Ashwin Vaidya +1
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 th…
Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection
Tetiana Gula, João P C Bertoldo
Anomaly detection (AD) in images, identifying significant deviations from normality, is a critical issue in computer vision. This paper introduces a novel approach to dimensionalit…
Heuristic Hyperparameter Choice for Image Anomaly Detection
Zeyu Jiang, João P. C. Bertoldo, Etienne Decencière
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 featur…
Visualization for Multivariate Gaussian Anomaly Detection in Images
Joao P C Bertoldo, David Arrustico
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 multivar…
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation
Joao P. C. Bertoldo, Santiago Velasco-Forero, Jesus Angulo +1
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…
[Reproducibility Report] Explainable Deep One-Class Classification
Joao P. C. Bertoldo, Etienne Decencière
Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without a…
A modular U-Net for automated segmentation of X-ray tomography images in composite materials
João P C Bertoldo, Etienne Decencière, David Ryckelynck +1
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,…