Textural Defect Detection Based on Fuzzy Label Co-occurrence Matrix
基于模糊类别共生矩阵的纹理疵点检测方法

In the texture image,the imperfect part is denoted as defect.Texture is usually depicted by a gray-level distribution along with a certain spatial interaction.Thus,gray-level co-occurrence matrix(GLCM) is an appropriate candidate to depict texture because of its capability of blending spatial interaction with gray-level distribution.However,GLCM is considered as deficient in discriminating the normal and abnormal parts of texture,and in computation efficiency as well.In order to overcome these drawbacks of GLCM,a method of fuzzy label co-occurrence matrix(FLCM) is proposed to detect the textural imperfection.In this method,textural features such as the probability density distribution of the gray levels,the intrinsic dominant orientation and periodicity in the texture,are extracted firstly to set some key parameters of FLCM,and then all gray-levels are classified into several textural tonal classes in a certain rule;the fuzzy membership degrees of each gray-level to each tonal class are computed based on the corresponding posteriori probability,finally the FLCMs are calculated and some simple features are extracted from the FLCMs,and outlier detection is applied to discriminate imperfection from normal texture.It is proved practically that this method is simpler and has better performance in detecting textural imperfection than GLCM.