In this paper we propose an efficient, non-iterative method for estimating optical flow. We develop a probabilistic framework that is appropriate for describing the inherent uncer...
Abstract. This contribution presents a novel approach to the challenging problem of model selection in motion estimation from sequences of images. New light is cast on parametric m...
This paper presents a new formulation of the problem of motion estimation which attempts to give solutions to classical problems in the field, such as detection of motion disconti...
The classical optical flow assumes that a feature point maintains constant brightness across the frames. For fluidtype motions such as smoke or clouds, the constant brightness ass...
This paper presents a robust and flexible framework for augmented reality which does not require instrumenting either the environment or the workpiece. A model-based visual track...