基于多特征融合图像分析技术的羊毛与羊绒鉴别

为快速准确地鉴别羊毛与羊绒,提出一种基于多特征融合的鉴别方法。首先利用光学显微镜及数码相机对羊毛与羊绒纤维进行图像采集,然后分别采用2种类型的预处理操作得到单根纤维图像与去除背景的纤维二值图像;其次通过灰度共生矩阵算法提取第1类预处理后羊毛与羊绒纤维图像的纹理特征参数,基于中轴线算法提取第2类预处理后纤维图像的直径形态特征参数;最后将纹理及形态特征参数融合成多维数组并通过K均值算法进行聚类识别。实验结果显示,与传统利用单一纤维特征提取算法进行识别的方法相比,该算法平均识别率可达到95.25%,识别率较高,可用于羊毛与羊绒纤维的自动分类识别。

Identification of wool and cashmere based on multi-feature fusion image analysis technology

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.