We study unsupervised learning of occluding objects in images of visual scenes. The derived learning algorithm is based on a probabilistic generative model which parameterizes obj...
This work presents a discriminative training method for
particle filters in the context of multi-object tracking. We
are motivated by the difficulty of hand-tuning the many
mode...
In this paper, we present a system including a novel component called programmable aperture and two associated post-processing algorithms for high-quality light field acquisition. ...
Chia-Kai Liang and Tai-Hsu Lin and Bing-Yi Wong a...
This paper describes a probabilistic multiple-hypothesis framework for tracking highly articulated objects. In this framework, the probability density of the tracker state is repr...
This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field...