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.
Biography: I received my Diploma degree in 2006 from the School of Rural and Surveying Engineering in NTUA, working in the Laboratory of Photogrammetry. I received my MSE degree in Artificial Intelligence and Robotics in 2012 from Sapienza University of Rome, working in ALCOR Lab.
As of Oct. 2012 I am enrolled in the Ph.D. program of Engineering in Computer Science of the University of Rome "La Sapienza", working in ALCOR Lab.