We address the problem of Bayesian estimation where the statistical relation between the signal and measurements is only partially known. We propose modeling partial Baysian knowl...
Kernel density estimation (KDE) has been used in many computational intelligence and computer vision applications. In this paper we propose a Bayesian estimation method for findin...
This paper proposes a novel model-guided segmentation framework utilizing a statistical surface wavelet model as a shape prior. In the model building process, a set of training sh...
Yang Li, Tiow Seng Tan, Ihar Volkau, Wieslaw L. No...
In this paper, we propose an energy functional to segment objects whose global shape is a priori known thanks to a statistical model. Our work aims at extending the variational ap...
Xavier Bresson, Pierre Vandergheynst, Jean-Philipp...
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...