Pierre Vandergheynst

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Visual short-term memory binding tasks are a promising early marker for Alzheimer's disease (AD). To uncover functional deficits of AD in these tasks it is meaningful to first study unimpaired brain function. Electroencephalogram recordings were obtained f ...

We consider the estimation of multiple time-domain sparse filters from echoic mixtures of several unknown sources, when the sources are sparse in the time-frequency domain. We propose a sparse filter estimation framework consisting of two steps: a) a clust ...

A new method of image segmentation based on building up a hierarchical tree through non-linear scale-space is proposed. Non-linear scale space creates from the original image the whole family of blurred images but keeps sharp region boundaries. This characteristic of preserving edges turns out to be extremely useful in choosing a linking strategy through scale.

Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along w ...

A number of new localized, multiscale transforms have recently been introduced to analyze data residing on weighted graphs. In signal processing tasks such as regularization and compression, much of the power of classical wavelets on the real line is deriv ...

Today's video codecs employ motion compensated prediction in combination with block matching techniques. These techniques, although achieving some level of adaptivity in their latest versions, continue to rely on the decomposition of frames on a set of artificial primitives: blocks. This paper presents a new approach to video coding. A geometrically adaptive image decomposition scheme using an over-complete basis is used to represent the scene. Using Matching Pursuit (MP), we are able to express local featu

The aim of this project is to find a motion estimation method that works in combination with block matching in order to reduce the visible artifacts. The proposed solution tries to extract the real motions taking place in a sequence. The developed algorithm is a region based motion estimator. We associate to regions of general shape motion parameters which describe an ane transformation on the plane. The traditional block matching is then used for smaller transformations. The developed algorithm is iterativ

This paper studies the problem of sparse signal approximation over redundant dictionaries. Our attention is focused on the minimization of a cost function where the error is measured by using the L1 norm, giving thus less importance to outliers. We show a constructive equivalence between the proposed minimization problem and Linear Programming. A recovery condition is then provided and an example illustrates the use of such a technique for denoising.

This article extends the concept of it compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, which is a composition of a random matrix of certain type and a deterministic ...

This paper investigates video coding with wavelet transforms applied in the temporal direction of a video sequence. The wavelets are implemented with the lifting scheme in order to permit motion compensation between successive pictures. We improve motion compensation in the lifting steps and utilize complementary motion-compensated signals. Similar to superimposed predictive coding with complementary signals, this approach improves compression efficiency. We investigate experimentally and theoretically comp

This paper analyzes the performance of the simple thresholding algorithm for sparse signal representations. In particular, in order to be more realistic we introduce a new probabilistic signal model which assumes randomness for both the amplitude and also ...

In this article we present a signal model for classification based on a low dimensional dictionary embedded into the high dimensional signal space. We develop an alternate projection algorithm to find the embedding and the dictionary and finally test the c ...

This paper proposes an adaptive entropy-constrained Matching Pursuit coefficient quantization scheme. The quantization scheme takes benefit of the inherent properties of Matching Pursuit streams where coefficients energy decreases along with the iteration number. The decay rate can moreover be upper-bounded with an exponential curve driven by the redundancy of the dictionary. An optimal entropy-constrained quantization scheme can thus be derived once the dictionary is known. We propose here to approximate t

In this paper, we propose to study the problem of localization of a dense set of people with a network of heterogeneous cameras. We propose to recast the problem as a linear inverse problem. The proposed framework is generic to any scene, scalable in the n ...

We propose a graph signal processing framework to overcome the computational burden of Tensor Robust PCA (TRPCA). Our framework also serves as a convex alternative to graph regularized tensor factorization methods. Our method is based on projecting a tenso ...

Numerous applications demand that we manipulate large sets of very high-dimensional signals. A simple yet common example is the problem of finding those signals in a database that are closest to a query. In this paper, we tackle this problem by restricting ...

In this paper we present a novel nonlinear video diffusion approach based on the fusion of information in audio and video channels. Both modalities are efficiently combined into a diffusion coefficient that integrates the basic assumption in this domain, i ...

In this paper we propose a novel and efficient model for compressed sensing of hyperspectral images. A large-size hyperspectral image can be subsampled by retaining only 3% of its original size, yet robustly recovered using the new approach we present here. Our reconstruction approach is based on minimizing a convex functional which penalizes both the trace norm and the TV norm of the data matrix. Thus, the solution tends to have a simultaneous low-rank and piecewise smooth structure: the two important prio

There has been an intense activity recently in the field of sparse approximations with redundant dictionaries, largely motivated by the practical performances of algorithms such as Matching Pursuit and Basis Pursuit. However, most of the theoretical result ...

We present a novel plenoptic sampling scheme that permits an efficient representation of the full light ray field in a space limited by a convex closed surface. We show that a convenient way to sample the light ray field around an observer consists in usin ...

Pierre Vandergheynst

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Locating Temporal Functional Dynamics of Visual Short-Term Memory Binding using Graph Modular Dirichlet Energy

Doubly sparse models for multiple filter estimation in sparse echoic environments

Segmentation of Natural Images by Non-linear Scale-Space linking

Graph Signal Processing: Overview, Challenges and Applications

On the Sparsity of Wavelet Coefficients for Signals on Graphs

Video Coding Using a Deformation Compensation Algorithm Based on Adaptive Matching Pursuit Image Decompositions

Dynamic Region and Block-Based Motion Estimation for Video Compression

Sparse Approximation by Linear Programming using an L1 Data-Fidelity Term

Compressed sensing and redundant dictionaries

Video Coding with Lifted Wavelet Transforms and Complementary Motion-Compensated Signals

Average case analysis of sparse recovery with Thresholding: New bounds based on average dictionary coherence

Dictionary Learning Based Dimensionality Reduction for Classification

Adaptive Entropy-Constrained Matching Pursuit Quantization

Sparsity Driven People Localization with a Heterogeneous Network of Cameras

Tensor Robust Pca On Graphs

On finding approximate nearest neighbours in a set of compressible signals

Audio-driven Nonlinear Video Diffusion

Joint trace/TV norm minimization: A new efficient approach for spectral compressive imaging

Matching Pursuit with Block Incoherent Dictionaries

Plenoptic Spherical Sampling