基于显微图像的动物纤维鉴别技术

羊绒与细羊毛的主要辨识依据是两者的表皮鳞片模式.该领域内常用的一项技术是分析纤维的SEM图像,通过鳞片边缘高度来区分两类纤维,但其成本高昂,且有8%的误差.该文提出区分两类纤维的新方法,首先将显微摄像系统获取的纤维图像处理成单像素宽度的二值骨架图,通过该二值骨架图提取纤维鳞片的4个相对形状参数,构建贝叶斯 分类模型.数值实验表明,尽管该模型是基于光学显微镜的,但其分类性能却相似于基于扣描电镜的模型,对羊绒与细羊毛(70S)的正确识别率达到90%.

Classification of Animal Fibers Based on Microscopic Images

Scale and pattern of cashmere and fine wool are different,which is used as a major reference to distinguish them.A commonly used technique is to analyze cuticle scale edge height(CSH)of fiber from SEM images.However, it is expensive and has an average error of 8%.A new method is presented in this paper.After the fiber images are captured with a CCD camera,they are transformed into skeletonzied binary images which are only one pixel wide and can show fiber and scale edge details.Four relative shape parameters of the fiber scale are extracted.A multi-parameter Bayes classification model is then developed.Numerical experiment results show that,by using an ordinary microscopy,the proposed Bayes model has the performance similar to that based on a scanning electronic microscopy in differentiating cashmere and fine wool(70 S),with accuracy rate approaching 90%.