Publications related to Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Factored Facade Acquisition using Symmetric Line Arrangements

We introduce a novel framework for image-based 3D reconstruction of urban buildings based on symmetry priors. Starting from image-level edges, we generate a sparse and approximate set of consistent 3D lines. These lines are then used to simultaneously dete ...

Wiley-Blackwell2012

3D Reconstruction Of Wave-Propagated Point Sources From Boundary Measurements Using Joint Sparsity And Finite Rate Of Innovation

Dimitri Nestor Alice Van De Ville, Thierry Blu, Ivana Jovanovic, Zafer Dogan

Reconstruction of point sources from boundary measurements is a challenging problem in many applications. Recently, we proposed a new sensing and non-iterative reconstruction scheme for systems governed by the three-dimensional wave equation. The points so ...

IEEE Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2012

Sparse diffusion LMS for distributed adaptive estimation

Ali H. Sayed

The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, ...

IEEE2012

Equivalence of synthesis and atomic formulations of sparse recovery

Volkan Cevher, Mitra Fatemi, Shayan Dashmiz

Recovery of sparse signals from linear, dimensionality reducing measurements broadly fall under two well-known formulations, named the synthesis and the analysis a ́ la Elad et al. Recently, Chandrasekaran et al. introduced a new algorithmic sparse recover ...

Ieee2012

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

A Constrained Latent Variable Model

Pascal Fua, Mathieu Salzmann, Raquel Urtasun, Aydin Varol

Latent variable models provide valuable compact representations for learning and inference in many computer vision tasks. However, most existing models cannot directly encode prior knowledge about the specific problem at hand. In this paper, we introduce a ...

Ieee2012

Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Pierre Vandergheynst, Mohammad Golbabaee

We propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measure- ments. Our reconstruction approach is based on a convex minimiza- tion which penalizes both the nuclear norm and the l2,1 mixed-norm of the d ...

Ieee2012

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias Seeger

Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher- ...

2011

Geodesic Active Fields

Dominique Zosso

Image registration is the concept of mapping homologous points in a pair of images. In other words, one is looking for an underlying deformation field that matches one image to a target image. The spectrum of applications of image registration is extremely ...

EPFL2011

Image Reconstruction from Compressed Linear Measurements with Side Information

Pascal Frossard, Vijayaraghavan Thirumalai

This paper proposes a joint reconstruction algorithm for compressed correlated images that are given under the form of linear measurements. We consider the particular problem where one image is selected as the reference image and it is used as the side inf ...

2011

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

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Factored Facade Acquisition using Symmetric Line Arrangements

3D Reconstruction Of Wave-Propagated Point Sources From Boundary Measurements Using Joint Sparsity And Finite Rate Of Innovation

Sparse diffusion LMS for distributed adaptive estimation

Equivalence of synthesis and atomic formulations of sparse recovery

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

A Constrained Latent Variable Model

Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Geodesic Active Fields

Image Reconstruction from Compressed Linear Measurements with Side Information

Sparse diffusion LMS for distributed adaptive estimation

3D Reconstruction Of Wave-Propagated Point Sources From Boundary Measurements Using Joint Sparsity And Finite Rate Of Innovation

Factored Facade Acquisition using Symmetric Line Arrangements

Equivalence of synthesis and atomic formulations of sparse recovery

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

A Constrained Latent Variable Model

Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Geodesic Active Fields

Image Reconstruction from Compressed Linear Measurements with Side Information