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.