A Texton-Based Algorithm of Texture Image Segmentation
基于纹理基元的图象分割

Texture segmentation is one of fundamental problems in image processing. An efficient and robust method is needed for various texture samples. Consequently, this paper presents a texton-based algorithm of texture image segmentation. At first, Harr wavelet is utilized as a transform tool so that texture subimages of different directions are produced. And then, we use double threshold technology to get edges and propose a new method to extract textons of four directions. Thereafter, textons are simplified to one dimension vector and reduced to twelve kinds. At last, Statistics and vector field are applied respectively in order to separate texture images from coarse to fine. The algorithm is simple and effective for a wide of textures. Through this way, texture images are not only separated, but also texture structure can be described, which is useful for high-level image processing tasks.