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The objective of this PhD thesis is the approximate computation of the solutions of the Spectral Problem associated with the Laplace operator on a compact Riemann surface without boundaries. A Riemann surface can be seen as a gluing of portions of the Hype ...
We propose a vector space approach for inverse rendering of a Lambertian convex object with distant light sources. In this problem, the texture of the object and arbitrary lightings are both to be recovered from multiple images of the object and its 3D mod ...
We propose a preliminary investigation on the benefits and limitations of classifiers based on sparse representations. We specifically focus on the union of subspaces data model and examine binary classifiers built on a sparse non linear mapping (in a redu ...
A computationally efficient technique for fringe analysis in digital holographic interferometry using a difference equation representation of the interference field is presented. The spatially varying coefficient of the difference equation is estimated acc ...
We study the longitudinal linear optimal perturbations (which maximize the energy gain up to a prescribed time T) to inviscid parallel shear flow, which present unbounded energy growth due to the lift-up mechanism. Using the phase invariance with respect t ...
Mesh editing under constraints is a challenging task with numerous applications in geometric modeling, industrial design, and architectural form finding. Recent methods support constraint-based exploration of meshes with fixed connectivity, but commonly la ...
This thesis addresses the problem of industrial real-time process optimization that suffers from the presence of uncertainty. Since a process model is typically used to compute the optimal operating conditions, both plant-model mismatch and process disturb ...
Given a nonsymmetric matrix A, we investigate the effect of perturbations on an invariant subspace of A. The result derived in this paper differs from Stewart's classical result and sometimes yields tighter bounds. Moreover, we provide norm estimates for t ...
We develop a least mean-squares (LMS) diffusion strategy for sensor network applications where it is desired to estimate parameters of physical phenomena that vary over space. In particular, we consider a regression model with space-varying parameters that ...
In this paper we introduce a budgeted knowledge transfer algorithm for non-homogeneous reinforcement learning agents. Here the source and the target agents are completely identical except in their state representations. The algorithm uses functional space ...