Based on scaling laws describing the statistical structure
of turbulent motion across scales, we propose a multiscale
and non-parametric regularizer for optic-flow estimation.
R...
Patrick H´eas, Etienne M´emin, Dominique Heitz, ...
We develop two new algorithms for tomographic reconstruction which incorporate the technique of equally-sloped tomography (EST) and allow for the optimized and flexible implementat...
Yu Mao, Benjamin P. Fahimian, Stanley Osher, Jianw...
We describe a new approach for creating concise high-level generative models from range images or other approximate representations of real objects. Using data from a variety of a...
This paper describes a novel application of Statistical Learning Theory (SLT) for motion prediction. SLT provides analytical VC-generalization bounds for model selection; these bo...
Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Ch...
The classical approach to depth from defocus uses two
images taken with circular apertures of different sizes. We
show in this paper that the use of a circular aperture
severely...