We propose a principled framework to model persistent motion in dynamic scenes. In contrast to previous efforts on object tracking and optical flow estimation that focus on local...
One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribution, in some class of probabilistic models...
Abstract. Semi-algebraic sets occur naturally when dealing with implicit models and boolean operations between them. In this work we present an algorithm to efficiently and in a ce...
Probabilistic I/O automata (PIOA) constitute a model for distributed or concurrent systems that incorporates a notion of probabilistic choice. The PIOA model provides a notion of ...
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning communit...