基于局部最优分析的纺织品瑕疵检测方法

针对复杂的含有周期变化图案的纺织品瑕疵检测,提出改进Markov随机场模型的无监督纺织品瑕疵检测方法.应用随机场实现周期性纺织品图像的瑕疵检测,利用Markov邻域特性,综合判断瑕疵区域.结合周期图像分割,确定Markov随机场最小图像块计算单元,降低算法的计算复杂度.在随机场势函数定义中,综合考虑相邻图像块的差异特性,结合Markov随机场的全局性判断瑕疵点的位置.引入模糊相似关系矩阵概念,求解改进后的模型参数,使所有图像块的局部能量达到最优.实验表明,文中方法对样本的查全率较高.

Fabric Defect Detection Based on Local Optimum Analysis

Aiming at the detection of textile defects with complex periodic patterns, an unsupervised fabric defect detection method based on modified Markov random field model is proposed. The defects of periodic textile images are detected and the areas of defect are judged via the Markov neighborhood feature. The minimum image block computing unit of Markov random field is determined by combining the segmentation of periodic image, and the computational complexity of the algorithm is reduced. In the definition of the random field potential function, the difference of adjacent image blocks is comprehensively taken into account. The location of defect area is judged by the global characteristics of the Markov random field. The concept of fuzzy similarity relation matrix is introduced to solve the parameters of the improved Markov random field model, and the local energy of all image blocks is optimized.Experiments show that the proposed defect detection method gains high recall.