基于多特征融合图像分析技术的羊毛与羊绒鉴别

For rapid identification of wool and cashmere, a method based on the multi-feature fusion for the fiber identification was proposed. Firstly, the images of wool and cashmere fibers were captured by an optical microscope and a digital camera. Secondly, two kinds of preprocessing operations were carried out respectively, and the binary images of single fiber image and background free fiber were obtained. Then, the texture parameters of the first kind of cashmere and wool fiber images were extracted by the gray level co-occurrence matrix algorithm and the diameter parameters of the second kinds of fiber images were extracted based on the central axis algorithm. Finally, the texture and morphological feature parameters were fused into multidimensional array and the clustering analysis was carried out by the K-means algorithm. The experimental results show that the average identification rate of the algorithm proposed can reach 95.25%. Compared with the conventional single fiber feature extraction algorithm, the recognition rate is high, which confirmed that this method can be used for automatic classification and identification of cashmere and wool fibers.