Bayesian forecasting models provide distributional estimates for random parameters, and relative to classical schemes, have the advantage that they can rapidly capture changes in ...
We introduce an alternative to the smoothing technique approach for constrained optimization. As it turns out for any given smoothing function there exists a modification with part...
The learning of probabilistic models with many hidden variables and nondecomposable dependencies is an important and challenging problem. In contrast to traditional approaches bas...
When viewed from a system of multiple cameras with nonoverlapping fields of view, the appearance of an object in one camera view is usually very different from its appearance in a...
This paper introduces a new method for shape registration by matching vector distance functions. The vector distance function representation is more flexible than the conventional...