CAJ | 학술논문

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%.
양융여세양모적주요변식의거시량자적표피린편모식.해영역내상용적일항기술시분석섬유적SEM도상,통과린편변연고도래구분량류섬유,단기성본고앙,차유8%적오차.해문제출구분량류섬유적신방법,수선장현미섭상계통획취적섬유도상처리성단상소관도적이치골가도,통과해이치골가도제취섬유린편적4개상대형상삼수,구건패협사 분류모형.수치실험표명,진관해모형시기우광학현미경적,단기분류성능각상사우기우구묘전경적모형,대양융여세양모(70S)적정학식별솔체도90%.
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%.