Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection

dc.contributor.authorArdelean, Andrei-Timoteien_US
dc.contributor.authorRückbeil, Patricken_US
dc.contributor.authorWeyrich, Timen_US
dc.contributor.editorEgger, Bernharden_US
dc.contributor.editorGünther, Tobiasen_US
dc.date.accessioned2025-09-24T10:38:09Z
dc.date.available2025-09-24T10:38:09Z
dc.date.issued2025
dc.description.abstractZero-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.en_US
dc.description.sectionheadersImaging and Image Processing
dc.description.seriesinformationVision, Modeling, and Visualization
dc.identifier.doi10.2312/vmv.20251240
dc.identifier.isbn978-3-03868-294-3
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/vmv.20251240
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/vmv20251240
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies->Anomaly detection
dc.subjectComputing methodologies
dc.subjectAnomaly detection
dc.titleQuantized FCA: Efficient Zero-Shot Texture Anomaly Detectionen_US
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