Class-Based Co-occurrence Matrix Texture defect detection method ZOU Chao Zhu Sen Xiao Li Abstract: According to the rules-texture features is proposed based on categories of co-occurrence matrix to describe the texture features, which will be well distinguished from the normal texture with defects. Analysis of the traditional GLCM texture features in the calculation for calculating the volume, and the disadvantage of poor resolution. In order to overcome the symbiotic Gray Moment Array in the calculation of capacity and the ability to distinguish the shortcomings of the definition of the categories of co-occurrence matrix. In the categories of co-occurrence matrix algorithm, first learn some basic features of the texture in order to determine the categories of some of the key parameters of co-occurrence matrix. Such as the texture of the probability density distribution of the main texture direction and cycles, as well as the classification criteria and other important parameters,

基于类别共生矩阵的纹理疵点检测方法.pdf

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