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Variational Template Matching with Statistical Fusion for Anomaly Detection in Patterned Structures

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

Anomaly detection in structured images is challenging in small-data settings where deep learning approaches are costly or impractical. Classical template matching is simple and interpretable but lacks robustness to geometric variations such as scale, rotation, and perspective. We propose a variational template matching framework that represents anomaly templates as a family of transformed instances and performs detection via normalized cross-correlation over this transformation space. To further improve robustness, we introduce a density-based statistical anomaly score derived from local inten

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