Regularization and Statistical Inverse Problems in Shape and Motion Modeling

Modeling and reconstruction of shape and motion are problems of fundamental importance in computer vision. Inverse Problem theory constitutes a powerful mathematical framework for dealing with ill-posed problems as the ones typically arising in shape and motion modeling. In this talk, I will present methods inspired by Inverse Problem theory, for dealing with four different shape and motion modeling problems. In particular, in the context of shape modeling, I will present a method for component-wise modeling of articulated objects and its application in computing 3D models of animals. Additionally, I will discuss the problem of modeling of specular surfaces via the properties of their material, and I will also present a model for confidence driven depth image fusion based on total variation regularization. Regarding motion, I will discuss a method for the recognition of human actions from motion capture data based on Nonparametric Bayesian models.