Digital Reality Lab

 
[Classifying Texture Anomalies at First Sight]

Classifying Texture Anomalies at First Sight

Andrei-Timotei Ardelean,  Tim Weyrich

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Abstract

The problem of detecting and localizing defects in images has been tackled with various approaches, including what are now called traditional computer vision techniques, as well as machine learning. Notably, most of these efforts have been directed toward the normality-supervised setting of this problem. That is, these algorithms assume the availability of a curated set of normal images, known to not contain any anomalies. The anomaly-free images constitute reference data, used to detect anomalies in a one-class classification setting. While this kind of data is easier to acquire than anomaly-annotated images, it is still costly or difficult to obtain in-domain data for certain applications. We address the problem of anomaly detection and localization under a training-set-free paradigm and do not require any anomaly-free reference data. Concretely, we introduced a truly zero-shot method that can localize anomalies in a single image of a previously unobserved texture class. Then, we develop a mechanism to leverage additional test images, which may contain anomalies. Furthermore, we extend our analysis to also include a categorization of the anomalies in the given population through clustering. Importantly, we focus our attention on textures and texture-like images as we develop an anomaly detection method for structural defects, rather than logical anomalies. This aligns with the proposed setting, which avoids the supervisory signal generally needed for detecting logical and semantical anomalies. This poster summarizes our recent line of research on localization and classification of anomalies in real-world texture images.

Citation Style:    Publication

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
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Related Publication

[Blind Localization and Clustering of Anomalies in Textures]
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]
[High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis]
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.
[Web Page][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]

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).


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