Xie Xianghua, Mirmehdi Majid
Department of Computer Science, University of Bristol, MVB 2.08, Woodland Road, Bristol BS8 1UB, UK.
IEEE Trans Pattern Anal Mach Intell. 2007 Aug;29(8):1454-64. doi: 10.1109/TPAMI.2007.1038.
We present an approach to detecting and localizing defects in random color textures which requires only a few defect free samples for unsupervised training. It is assumed that each image is generated by a superposition of various-size image patches with added variations at each pixel position. These image patches and their corresponding variances are referred to here as textural exemplars or texems. Mixture models are applied to obtain the texems using multiscale analysis to reduce the computational costs. Novelty detection on color texture surfaces is performed by examining the same-source similarity based on the data likelihood in multiscale, followed by logical processes to combine the defect candidates to localize defects. The proposed method is compared against a Gabor filter bank-based novelty detection method. Also, we compare different texem generalization schemes for defect detection in terms of accuracy and efficiency.
我们提出了一种检测和定位随机颜色纹理中缺陷的方法，该方法在无监督训练时仅需要少量无缺陷样本。假设每个图像由各种大小的图像块叠加生成，每个像素位置添加了变化。这些图像块及其相应的方差在这里被称为纹理样本或纹理单元。应用混合模型通过多尺度分析来获得纹理单元，以降低计算成本。基于多尺度数据似然性的同源相似性检查对颜色纹理表面进行新奇性检测，然后通过逻辑过程组合缺陷候选区域以定位缺陷。将所提出的方法与基于Gabor滤波器组的新奇性检测方法进行比较。此外，我们从准确性和效率方面比较了不同的纹理单元泛化方案用于缺陷检测的情况。
