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首页> 外文会议>IEEE International Conference on Image Processing >SCALE-INVARIANT ANOMALY DETECTION WITH MULTISCALE GROUP-SPARSE MODELS
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SCALE-INVARIANT ANOMALY DETECTION WITH MULTISCALE GROUP-SPARSE MODELS

机译：使用多尺度组稀疏型号的尺度不变异常检测

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The automatic detection of anomalies, defined as patterns that are not encountered in representative set of normal images, is an important problem in industrial control and biomedical applications. We have shown that this problem can be successfully addressed by the sparse representation of individual image patches using a dictionary learned from a large set of patches extracted from normal images. Anomalous patches are detected as those for which the sparse representation on this dictionary exceeds sparsity or error tolerances. Unfortunately, this solution is not suitable for many real-world visual inspection-systems since it is not scale invariant: since the dictionary is learned at a single scale, patches in normal images acquired at a different magnification level might be detected as anomalous. We present an anomaly-detection algorithm that learns a dictionary that is invariant to a range of scale changes, and overcomes this limitation by use of an appropriate sparse coding stage. The algorithm was successfully tested in an industrial application by analyzing a dataset of Scanning Electron Microscope (SEM) images, which typically exhibit different magnification levels.
机译：异常的自动检测，定义为在代表性的正常图像集中不遇到的模式，是工业控制和生物医学应用中的一个重要问题。我们已经表明，通过使用从正常图像中提取的大组修补程序中学到的单个图像补丁的稀疏表示，可以成功解决该问题。检测到异常贴片作为该字典上稀疏表示超过稀疏性或误差容限的斑块。不幸的是，这种解决方案不适用于许多真实世界的视觉检查系统，因为它不是缩放不变的：由于字典以单个刻度学习，因此可以在不同放大级别获取的正常图像中的贴片被检测为异常。我们提出了一种异常检测算法，该算法学习不变的字典到一系列比例变化，并通过使用适当的稀疏编码阶段来克服这种限制。通过分析扫描电子显微镜（SEM）图像的数据集在工业应用中成功测试了该算法，该数据集通常表现出不同的放大率。

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