Intelligent Animal Fiber Classification with Artificial Neural Networks
Scale pattern of cashmere and fine wool is different and that is a major reference distinguishing them from each other.A technique commonly used consists of analyzing their SEM image for detecting cuticle scale edge height (CSH) of fiber.However,the method is expensive and only a small quantity of fiber samples is detected,which make the average error approach 8 percent.In this paper,a new method is presented.The color light microscope images of fiber captured by CCD camera are transformed into skelrtonzied binary images only having one pixel wide and showing only fiber and scale edge details.Then four basic shape parameters of fiber scale are measured and a database composed of numerical data of four comparable indexes,which are fiber diameter,scale interval,normalized scale perimeter and normalized scale area,is established.A LVQ neural network classification model,including four input nodes,sixteen hidden nodes and two output nodes,are developed on the basis of comparable indexes.The simulation testing results show that whether on training set or testing set,the model can always distinguish cashmere from fine wool (70s) effectively and the average classification accuracy are higher than 93 percent.
threshold morphological operations scale pattern LVQ neural network
Xian-Jun Shi Wei-Dong Yu
College of Science,Wuhan University of Science and Engineering,Wuhan,ChinaTextile materials and tech Textile materials and technology lab,Donghua University,Shanghai,China
国际会议
International Conference on Modelling,Identification and Control(模拟、鉴定、控制国际会议)
上海
英文
2008-06-29（万方平台首次上网日期，不代表论文的发表时间）