High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis

Abstract

We propose a novel method for Zero-Shot Anomaly Localization on textures. The task refers to identifying abnormal regions in an otherwise homogeneous image. To obtain a high-fidelity localization, we leverage a bijective mapping derived from the 1-dimensional Wasserstein Distance. As opposed to using holistic distances between distributions, the proposed approach allows pinpointing the non-conformity of a pixel in a local context with increased precision. By aggregating the contribution of the pixel to the errors of all nearby patches, we obtain a reliable anomaly score estimate. We validate our solution on several datasets and obtain more than a 40% reduction in error over the previous state of the art on the MVTec AD dataset in a zero-shot setting.

Citation Style:    Display Alpha ACM SIGGRAPH IEEEtran Publication

High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis. Andrei-Timotei Ardelean, Tim Weyrich. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 1134–1144, January 2024.Andrei-Timotei Ardelean and Tim Weyrich. High-fidelity zero-shot texture anomaly localization using feature correspondence analysis. In Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 2024.Ardelean, A.-T., and Weyrich, T. 2024. High-fidelity zero-shot texture anomaly localization using feature correspondence analysis. In Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).A.-T. Ardelean and T. Weyrich, “High-fidelity zero-shot texture anomaly localization using feature correspondence analysis,” in Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Jan. 2024. PDF (37 MB) Low-res PDF (2.0 MB) Suppl. Material, PDF only (1.4 MB) Suppl. Material, Full Archive (90 MB) Short Video (55 MB) Source Code BibTeX arXiv Versions Open-Access Version

Related Publications

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]

Blind Localization and Clustering of Anomalies in Textures. Andrei-Timotei Ardelean, Tim Weyrich. Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, to appear, June 2024.Andrei-Timotei Ardelean and Tim Weyrich. Blind localization and clustering of anomalies in textures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, June 2024.Ardelean, A.-T., and Weyrich, T. 2024. Blind localization and clustering of anomalies in textures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.A.-T. Ardelean and T. Weyrich, “Blind localization and clustering of anomalies in textures,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Jun. 2024. [Web Page][PDF (6 MB)][Suppl. Material (461 KB)][Source Code][BibTeX][arXiv Version]

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).