We present a new class of statistical models for part-based object recognition. These models are explicitly parametrized according to the degree of spatial structure that they can ...
David J. Crandall, Pedro F. Felzenszwalb, Daniel P...
: This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning meth...
We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matchin...
In this paper, we present a modified snake model for the problem of general video object tracking. We introduce a new external force into the snake equation based on the predictiv...
In this paper, we first present an Object Composition Petri Nets (OCPN) based model methodology for describing the dynamic behaviour of the multiple video objects and user interac...