We propose a novel probabilistic framework for learning
visual models of 3D object categories by combining appearance
information and geometric constraints. Objects are
represen...
In this paper, we represent human actions as short sequences of atomic body poses. The knowledge of body pose is stored only implicitly as a set of silhouettes seen from multiple ...
Abhijit S. Ogale, Alap Karapurkar, Yiannis Aloimon...
Abstract. We present a biologically inspired vision system able to incrementally learn multiple visual categories by interactively presenting several hand-held objects. The overall...
Stephan Kirstein, Heiko Wersing, Horst-Michael Gro...
We present a novel motion-based approach for the part determination and shape estimation of a human’s body parts. The novelty of the technique is that neither a prior model of t...
Today's category-level object recognition systems largely focus on fronto-parallel views of objects with characteristic texture patterns. To overcome these limitations, we pr...