Pierre Vandergheynst

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In this paper, we address the problem of finding image decompositions that allow good compression performance, and that are also efficient for face authentication. We propose to decompose the face image using Matching Pursuit and to perform the face authentication in the compressed domain using a MLP (Multi-Layer Perceptron) classifier. We provide experimental results and comparisons with PCA and LDA systems on the multi-modal benchmark database BANCA using its associated protocol.

Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric tele-cardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, ...

We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements. The rows and columns of such matrices belong to the span of the first few eigenvectors of the graphs constructed between their rows and columns. We leverage this property to recover the non-linear low-rank structures efficiently from sampled data measurements, with a low cost (linear in n). First, a Resrtricted Isometry Property (RIP) condition is introduced for efficient un

This chapter discusses the problem of coding images using very redundant libraries of waveforms, also referred to as dictionaries. We start with a discussion of the shortcomings of classical approaches based on orthonormal bases. More specifically, we show why these redundant dictionaries provide an interesting alternative for image representation. We then introduce a special dictionary of 2-D primitives called anisotropic refinement atoms that are well suited for representing edge dominated images. Using a

This paper presents a rate-distortion analysis for a simple horizon edge image model. A quadtree with anisotropy and rotation is performed in this kind of image, giving a toy model for a non-linear adaptive coding technique, and its rate-distortion behavior is studied. The effect of refining the quadtree decomposition in the Rate-Distortion decay is also studied.

Super-resolution is the task of creating an high resolution image from a low resolution input sequence. To overcome the difficul- ties of fine image registration, several methods have been proposed exploiting the non-local intuition, i.e. any datapoint can ...

Ultra-low-power design has been a challenging area for design of the sensor front-ends especially in the area of Wireless Body Sensor Nodes (WBSN), where a limited amount of power budget and hardware resources are available. Since introduction of Compresse ...

We propose a method for signal reconstruction in semi-parametric dictionaries. The proposed algorithm estimates both the signal decomposition and the intrinsic parameters of the dictionary during the reconstruction process. Some results about the convergence of the algorithm are presented. The method is used here for the joint reconstruction of a set of misaligned images. Experiments show that the proposed algorithm accurately recovers the set of images and is robust to occlusions and misalignments. This me

We describe S2LET, a fast and robust implementation of the scale-discretised wavelet transform on the sphere. Wavelets are con- structed through a tiling of the harmonic line and can be used to probe spatially localised, scale-depended features of signals ...

Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique we develop a principled compressed sensing framework for quantitative MRI. The three key components are: a random pulse excitation sequence following the MRF technique; a r ...

This paper introduces p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latter ...

We present a novel method for robust reconstruction of the image of a moving object from incomplete linear measurements. We assume that only few measurements of this object can be acquired at different instants and model the correlation between measurement ...

In this work we present a new greedy algorithm for sparse approximation called LocOMP. LocOMP is meant to be run on local dictionaries made of atoms with much shorter supports than the signal length. This notably encompasses shift-invariant dictionaries an ...

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 ...

We propose a novel non-linear video diffusion approach which is able to focus on parts of a video sequence that are relevant for applications in audio-visual analysis. The diffusion process is controlled by a diffusion coefﬁcient based on an estimate of th ...

This paper shows introduces the use sensing dictionaries for p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We do both a worst case and average case recovery analyses of this ...

This paper addresses the problem of sensing or recovering a signal s, captured by distributed low-complexity sensors. Each sensor observes a noisy version of the signal of interest, and independently forms an approximant of its observation. This approximan ...

Visualizing high-dimensional data has been a focus in data analysis communities for decades, which has led to the design of many algorithms, some of which are now considered references (such as t-SNE for example). In our era of overwhelming data volumes, the scalability of such methods have become more and more important. In this work, we present a method which allows to apply any visualization or embedding algorithm on very large datasets by considering only a fraction of the data as input and then extendi

Given a two-dimensional smooth manifold M and a bijective pro jection p from M on a ﬁxed plane (or a subset of that plane), we explore systematically how a wavelet transform (WT) on M may be generated from a plane WT by the inverse projection. Examples whe ...

The long-standing analog-to-digital conversion paradigm based on Shannon/Nyquist sampling has been challenged lately, mostly in situations such as radar and communication signal processing where signal bandwidth is so large that sampling architectures cons ...

Pierre Vandergheynst

Chattez avec Graph Search

Face Authentication using Client-specific Matching Pursuit

Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

Compressive PCA for Low-Rank Matrices on Graphs

Image coding using redundant dictionaries

A generalized Rate-Distortion limit for edge representation

Fully non-local super-resolution via spectral hashing

Ultra-low-power ECG front-end design based on compressed sensing

Robust joint reconstruction of misaligned images using semi-parametric dictionaries

S2LET: A code to perform fast wavelet analysis on the sphere

A Compressed Sensing Framework for Magnetic Resonance Fingerprinting

Average case analysis of multichannel thresholding

Joint reconstruction of misaligned images from incomplete measurements for cardiac MRI

Fast orthogonal sparse approximation algorithms over local dictionaries

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

Audio-based nonlinear video diffusion

Multichannel thresholding with sensing dictionaries

Distributed sensing of noisy signals by thresholding of redundant expansions

Compressive Embedding and Visualization using Graphs

Wavelet transform on manifolds: old and new approaches

Design and Exploration of Low-Power Analog to Information Conversion Based on Compressed Sensing