为了快速地进行羊毛、羊绒的区分和检测，提出了电镜羊戎毛图象的自动识别方法。该方法先用自动阈值法对图象进行二值化，然后用动态聚类的方法检测每根羊绒毛的边界线，再由边界线侵害不同的羊绒毛；接着用Canny算子提取边缘，并进行后处理，在边缘图上，根据羊绒毛图象的鳞片特性，提取羊绒毛的细度和鳞片长度等特征；最后由特征参数通过Bayes判别法进行识别。实验结果表明，该系统对羊绒毛的识别，不仅快速准确，而且与以往的系统相比，在精度和速度上都有显著的提高。

Wool fibers play a very important role in the clothing industry. Wool fibers mainly include two types:wool and cashmere. Due to different property, they have widely different prices. However,it is always a challenging task to differentiate and recognize wool and cashmere. This paper presents an automatic recognition scheme for the wool fiber images by the electron microscope. At first the wool fibers are segmented from the background by a global thresholding method. Using the dynamic clustering method, the boundary lines of each wool fiber in the image are detected. According to these lines, different wool fibers are divided apart. Then we use Canny's algorithm to detect the edges of each wool fiber and do the post-processing. Using the character of the scales on the surface of the wool fiber, the features of the wool fiber such as the fineness and the length of the scale on the edge images are extracted. Owing to these feature parameters, we finally recognize whether a wool fiber is wool or cashmere in terms of the Bayes DecisionRule. Experiments demonstrate that the system works quickly and effectively, and has remarkable advantages in comparison with the previous systems.