Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Zero-shot anomaly localization is a rising field in computer vision research, with important progress in recent years. This work focuses on the problem of detecting and localizing anomalies in textures, where anomalies can be defined as the regions that deviate from the overall statistics, violating the stationarity assumption. The main limitation of existing methods is their high running time, making them impractical for deployment in real-world scenarios, such as assembly line monitoring. We propose a real-time method, named QFCA, which implements a quantized version of the feature correspondence analysis (FCA) algorithm. By carefully adapting the patch statistics comparison to work on histograms of quantized values, we obtain a 10× speedup with little to no loss in accuracy. Moreover, we introduce a feature preprocessing step based on principal component analysis, which enhances the contrast between normal and anomalous features, improving the detection precision on complex textures. Our method is thoroughly evaluated against prior art, comparing favorably with existing methods. Project page: reality.tf.fau.de/pub/ardelean2025quantized.html.
Description
CCS Concepts: Computing methodologies->Anomaly detection
@inproceedings{10.2312:vmv.20251240,
booktitle = {Vision, Modeling, and Visualization},
editor = {Egger, Bernhard and Günther, Tobias},
title = {{Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection}},
author = {Ardelean, Andrei-Timotei and Rückbeil, Patrick and Weyrich, Tim},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {10.2312/vmv.20251240}
}