S5 (2013-2014)

Hyperspectral image superresolution: An edge-preserving convex formulation

Hyperspectral remote sensing images (HSIs) are characterized by having a low spatial resolution and a high spectral resolution, whereas multispectral images (MSIs) are characterized by low spectral and high spatial resolutions. These complementary characteristics have stimulated active research in the … Read More

Orbit Regularization

We propose a general framework for regularization based on group-induced majorization. In this framework, a group is defined to act on the parameter space and an orbit is fixed; to control complexity, the model parameters are confined to the convex … Read More

Distributed detection over random networks

A team of agents collaborate to distinguish between two states of nature. Agents receive private measurements and exchange messages with neighbors to collectively solve the detection problem. We consider the challenging scenario of communication networks with time-variant random topologies thereby … Read More

Multidimensional wavelets and invariance principles for the analysis of bioimages

In this talk, we shall advocate the use of wavelets for the processing and analysis of images in biomicroscopy. We start with a short tutorial on wavelet bases, emphasizing the fact that they provide a concise multiresolution representation of signals … Read More

Learning Temporal-Dependent Ranking Models

Web archives already hold together more than 534 billion files and this number continues to grow as new initiatives arise. Searching on all versions of these files acquired throughout time is challenging, since users expect as fast and precise answers … Read More

Beyond the Mean Field Approximation for Inference in Multi-Layer Perceptrons

Interest in Multi-Layer Perceptrons (MLPs) has spiked in recent years due to their central role in deep learning technologies. MLPs can be seen as a trainable multi-input multi-output function constructed by composing linear and non-linear functions organized into layers of … Read More

Object uncategorized Recognition through RGB-D Data

Depth sensing technology of existing RGB-D sensors (e.g. Kinect), is now capable of capturing reliable 3D information of our world in real-time. So far, this availability of Depth along with RGB Information has led several researchers to prove the usefulness … Read More

Towards Model Independent Image Denoising and Reconstruction

In image denoising and reconstruction, it is natural that the algebraic and statistical model of the observation be taken into account to formulate the optimization problems. There has been a lot of recent literature devoted to the respective denoising, deconvolution, … Read More

Identification of Hybrid Systems with Particle Filtering and Expectation Maximization

This talk addresses the problem of parameter identification for a class of hybrid systems with continuous states and discrete time-varying parameters that can take different values from a finite set at each time instance. The identification of such systems typically … Read More

Fast gradient methods for distributed optimization

We present distributed optimization algorithms for minimizing the sum of convex functions, each one being the local cost function of an agent in a connected network. This finds applications in distributed learning, consensus, spectrum sensing for cognitive radio networks, resource … Read More