An Efficient Iterative Thresholding Algorithms for Color Images of Cotton Foreign Fibers

The goal of color image segmentation is to divide the image into homogeneous regions. Thresholding is a commonly used technique for image segmentation. Thresholding assumes that image present a number of components, each of a nearly homogeneous value, and that one can separate the components by a proper choice of intensity threshold. In this paper, we present an efficient iterative algorithm for finding optimal thresholds. In the first step, color images were captured, and the edge of color images were detected by edge detection method. In the second step, color images were converted into a gradient map, and then the regular of experience values were analyzed, at last the best threshold of the gradient map was chosen by selecting the best experience value iteratively. The experiment results indicate that the best threshold selection of the gradient map can precisely segment the high-resolution color images of cotton foreign fibers.