Image Segmentation of Cotton Based on YCbCcr Color Space and Fisher Discrimination Analysis

Abstract

For cotton harvesting robot,the cotton image segmentation is one of the key technologies.In this paper,under HSV,HIS,and YCbCr color spaces respectively,according to the difference between cotton color and background color,the various objects(cotton batting,cotton branches,soil etc.) in the sample images were classified,and then the pixel value of every category in different samples was extracted based on the classification result.In the following,the rule that the dispersion is biggest between different classes and smallest within the same class was used to calculate the Fisher discrimination vector and the center of mass in every class.Finally,image segmentation was carried out based on the criterion of pixel value close to the center of mass.The result showed that the least segmentation noise was obtained in the YCbCr color space,in which the method of labeling for self-adapting denoising was need.The simulation showed that the cotton could be separated exactly from the background by the above algorithm whether the cotton was exposed to the sunlight or the shadow.A total of that 136 cotton images were segmented with an accuracy of 90.44% in YCbCr color space.