Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection

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.

Citation Style:    Display Alpha ACM SIGGRAPH IEEEtran Publication

Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection. Andrei-Timotei Ardelean, Patrick Rückbeil, Tim Weyrich. Vision, Modeling, and Visualization (VMV), September 29 – October 01, Erlangen, Germany, 2025.Andrei-Timotei Ardelean, Patrick Rückbeil, and Tim Weyrich. Quantized FCA: Efficient zero-shot texture anomaly detection. In 30th Intl. Conference on Vision, Modeling, and Visualization (VMV), September 2025.Ardelean, A.-T., Rückbeil, P., and Weyrich, T. 2025. Quantized FCA: Efficient zero-shot texture anomaly detection. In 30th Intl. Conference on Vision, Modeling, and Visualization (VMV).A.-T. Ardelean, P. Rückbeil, and T. Weyrich, “Quantized FCA: Efficient zero-shot texture anomaly detection,” in 30th Intl. Conference on Vision, Modeling, and Visualization (VMV), Sep. 2025. BibTeX

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	Classifying Texture Anomalies at First Sight. Andrei-Timotei Ardelean, Tim Weyrich. ACM SIGGRAPH Posters '24, July 27–August 01, Denver, CO, USA, 2024.Andrei-Timotei Ardelean and Tim Weyrich. Classifying texture anomalies at first sight. In ACM SIGGRAPH 2024 Posters, SIGGRAPH ’24, New York, NY, USA, July 2024. Association for Computing Machinery.Ardelean, A.-T., and Weyrich, T. 2024. Classifying texture anomalies at first sight. In ACM SIGGRAPH 2024 Posters, Association for Computing Machinery, New York, NY, USA, SIGGRAPH ’24.A.-T. Ardelean and T. Weyrich, “Classifying texture anomalies at first sight,” in ACM SIGGRAPH 2024 Posters, ser. SIGGRAPH ’24. New York, NY, USA: Association for Computing Machinery, Jul. 2024. [Online]. Available: https://doi.org/10.1145/3641234.3671071 [Web Page][PDF (3.0 MB)][Poster PDF (16 MB)][BibTeX]
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Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956585 (PRIME ITN).