提出一种纹理基平滑分解方法，有效检测带纹理背景的图像异常。
High Dimensional Data Decomposition for Anomaly Detection of Textured Images
- 基于准周期性建模纹理基函数，提取图像纹理模式。
- 在仿真与真实数据集上误检率更低，仅需小规模训练数据。
- 适合工业质检中纹理复杂、异常稀疏的场景。
在各类高维数据中，图像在制造系统中具有关键作用，高效图像异常检测已成为核心技术。然而，传统方法在纹理缺陷图像上存在误检率高、鲁棒性差、依赖大规模结构化数据等问题。本文提出纹理基集成平滑分解（TBSD）方法，针对具有平滑背景和稀疏异常的纹理图像实现高效异常检测。通过研究准周期性的数学表述及其理论性质，实现图像纹理估计。TBSD包括两个主要过程：第一阶段学习纹理基函数以有效提取准周期性纹理特征；第二阶段利用纹理基作为先验知识，防止纹理误判并高精度捕捉潜在异常。该方法在仿真与真实数据集上均优于基准模型，具备更低误检率、更小训练数据需求及更优的异常检测性能。
原文摘要 · Abstract (English)
In the realm of diverse high-dimensional data, images play a significant role across various processes of manufacturing systems where efficient image anomaly detection has emerged as a core technology of utmost importance. However, when applied to textured defect images, conventional anomaly detection methods have limitations including non-negligible misidentification, low robustness, and excessive reliance on large-scale and structured datasets. This paper proposes a texture basis integrated smooth decomposition (TBSD) approach, which is targeted at efficient anomaly detection in textured images with smooth backgrounds and sparse anomalies. Mathematical formulation of quasi-periodicity and its theoretical properties are investigated for image texture estimation. TBSD method consists of two principal processes: the first process learns the texture basis functions to effectively extract quasi-periodic texture patterns; the subsequent anomaly detection process utilizes that texture basis as prior knowledge to prevent texture misidentification and capture potential anomalies with high accuracy.The proposed method surpasses benchmarks with less misidentification, smaller training dataset requirement, and superior anomaly detection performance on both simulation and real-world datasets.
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