In this work we present a novel approach for learning nonhomogenous textures without facing the unlearning problem. Our learning method mimics the human behavior of selective lear...
Abstract. Motion segmentation methods often fail to detect the motions of low textured regions. We develop an algorithm for segmentation of low textured moving objects. While usual...
In many real-world object recognition applications, texture plays a very important role. Much research has gone into texture-based segmentation methods, which focus on finding the...
David Van Hamme, Peter Veelaert, Wilfried Philips,...
Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image ...
This paper presents a statistical model for textures that uses a non-negative decomposition on a set of local atoms learned from an exemplar. This model is described by the varianc...