The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditi...
In this paper, we proposed a fast and accurate human pose estimation framework that combines top-down and bottom-up methods. The framework consists of an initialization stage and a...
Yanchao Su, Haizhou Ai, Takayoshi Yamashita, Shiho...
This paper presents a nonparametric approach to labeling
of local image regions that is inspired by recent developments
in information-theoretic denoising. The chief novelty
of ...
In this paper, we propose a series of techniques to enhance the computational performance of existing Belief Propagation (BP) based stereo matching that relies on automatic estima...
Shafik Huq, Andreas Koschan, Besma R. Abidi, Mongi...