Cotton image segmentation method for online foreign fiber inspection

Image segmentation is a key technology for foreign fiber inspection in cotton based on machine vision. The image of cotton containing foreign fiber has a feature of that the background (cotton fiber) is homogeneous and has a normal gray-level distribution; the object (foreign fiber) is smaller, darker than the background but its gray-level distributes in a wide range. In this paper, a Background Estimation Thresholding(BET) method was presented to segment the objects from such kind of cotton images. Three typical kinds of cotton images were selected for the use of experiments and compared with Otsu method. BET method obtained better segmentation results than the Otsu’s and was implemented fast, which consumed only 8.46s for 1 million times of segmentation. The experimental results show that the BET is effective and fast, and can be used in the online foreign fiber inspection in volumes of cotton.