Automatic Recognition Method for Wool Fiber Images of an Electron Microscope
电镜羊绒毛图象自动识别方法研究

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.