We develop an optical flow estimation framework that focuses on motion estimation over time formulated in a Dynamic Bayesian Network. It realizes a spatiotemporal integration of ...
Volker Willert, Marc Toussaint, Julian Eggert, Edg...
This paper considers a specific problem of visual perception of motion, namely the problem of visual detection of independent 3D motion. Most of the existing techniques for solvin...
When estimating the dense motion field of a video sequence, if little is known or assumed about the content, a limited constraint approach such as optical flow must be used. Since...
We present a method for estimating the shape of a deformable model using the least-squares residuals from a model-based optical flow computation. This method is built on top of an...
This paper deals with estimation of dense optical flow
and ego-motion in a generalized imaging system by exploiting
probabilistic linear subspace constraints on the flow.
We dea...
Richard Roberts (Georgia Institute of Technology),...