Automatic identification of ramie and cotton fibers based on iodine blue reaction,Part II: Automatic identification of ramie and cotton using color,texture,shape,and stripes

Abstract

A new method of automatically identifying ramie and cotton fibers using analysis of shape, color, texture, and surface stripes is here introduced. The shape, color, texture, and stripe features of the fibers were extracted by transformation from red, green, blue images to hue saturation intensity images and then to grayscale and binary images, by segmentation of the fiber from background, by edge detection of the outline of fibers, and by stripes on the surface of the fiber. Eighteen characteristic parameters suitable for identification were selected according to their probability distribution curves. A three-layer multilayer perceptron artificial-neural-network-based prediction system is here presented as a means of distinguishing cotton fibers from ramie fibers. The system training was carried out using a back propagation algorithm. The proposed system was tested on more than 2000 cotton and more than 2000 ramie fibers. The experimental results showed that the overall tolerance for false identification of cotton or ramie fiber was under 5%.