Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
—In this paper, we study the capacity-achieving input covariance matrices for the jointly-correlated (or the Weichselberger) Rician fading multiple-input multiple-output (MIMO) a...
Chao-Kai Wen, Shi Jin, Kai-Kit Wong, Jung-Chieh Ch...
—When reconstructing a specific type or class of object using stereo, we can leverage prior knowledge of the shape of that type of object. A popular class of object to reconstru...
We propose a framework to learn statistical shape models for faces as piecewise linear models. Specifically, our methodology builds upon primitive active shape models(ASM) to hand...
This paper presents novel methods for classifying images based on knowledge discovered from annotated images using WordNet. The novelty of this work is the automatic class discove...