Unsupervised Extraction of High Contrast Texture from Fine-Grained Backgrounds

To reduce common inaccuracies in the segmentation of high-contrast textures from fine-grained backgrounds,a two-channel unsupervised approach for texture extraction was proposed in this paper.Without changing the locations of target boundaries,edges and regional gray-scale features of high-contrast texture were acquired through the nonlinear diffusion of pixel intensities,horizontal gradients and vertical gradients of the image.In addition,a two-channel active contour model that contains fuzzy information and adjustment factor was established.In this model,the feature with larger differences between textures was taken as the leading term to drive the evolution of segmentation curves.Moreover,the level-set method was employed to achieve unsupervised extraction.Experimental results on various textures demonstrated the improvements in terms of segmentation performance and computational efficiency.