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...
In this paper, a Bayesian LBP operator is proposed. This operator is formulated in a novel Filtering, Labeling and Statistic (FLS) framework for texture descriptors. In the framew...
Traditional background modeling and subtraction methods have a strong assumption that the scenes are of static structures with limited perturbation. These methods will perform poo...
We develop and demonstrate an object recognition system capable of accurately detecting, localizing, and recovering the kinematic configuration of textured animals in real images....
Abstract— Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesising and capturing the characteristics of a wide variety of textures, f...