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 10x 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: https://reality.tf.fau.de/pub/ardelean2025quantized.html

翻译：零样本异常定位是计算机视觉研究中的一个新兴领域，近年来取得了重要进展。本研究聚焦于纹理中的异常检测与定位问题，其中异常可定义为偏离整体统计特性、违反平稳性假设的区域。现有方法的主要局限在于其较高的运行时间，使其难以在实际场景（如装配线监控）中部署。我们提出了一种名为QFCA的实时方法，它实现了特征对应分析（FCA）算法的量化版本。通过精心调整块统计比较机制，使其工作于量化值的直方图上，我们在几乎不损失精度的情况下获得了10倍的加速。此外，我们引入了基于主成分分析的特征预处理步骤，增强了正常特征与异常特征之间的对比度，从而提升了复杂纹理上的检测精度。我们的方法经过与现有技术的全面对比评估，结果优于现有方法。项目页面：https://reality.tf.fau.de/pub/ardelean2025quantized.html