Learning probabilistic deformation models from image sequences

Learning mixtures of smooth, nonuniform deformation models for probabilistic image matching

Learning Image Statistics for Bayesian Tracking

This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects in image sequences. We focus on the probabilistic tracking of people and learn models of how they appear and move in images. In particular, we learn the likelihood of observing various spatial and temporal filter respon...

A Probabilistic Model of Learning Fields in Islamic Economics and Finance

In this paper an epistemological model of learning fields of probabilistic events is formalized. It is used to explain resource allocation governed by pervasive complementarities as the sign of unity of knowledge. Such an episteme is induced epistemologically into interacting, integrating and evolutionary variables representing the problem at hand. The end result is the formalization of a p...

Learning structure and deformation modes of nonrigid objects in long image sequences

In this paper, we present an original approach for an unsupervised learning of the structure and deformation modes of 2D moving objects in long image sequences. The object representation relies on a statistical description of the deformations applied to a prototype shape. The optimal bayesian estimate of the deformation process is obtained by maximizing a non-linear joint probability distributi...

Learning Probabilistic Structure for Human Motion Detection

Decomposable triangulated graphs have been shown to be efficient and effective for modeling the probabilistic spatio-temporal structure of brief stretches of human motion. In previous work such model structure was handcrafted by expert human observers and labeled data were needed for parameter learning. We present a method to build automatically the structure of the decomposable triangulated gr...