Pierre Vandergheynst

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A new plenoptic sensor inspired from the visual system of flying insects is introduced. This sensor is referred to as the Panoptic camera. This system has two distinguished features. First, it is an omnidirectional camera capable of recording light information around it. Second, it is a polydioptric system where each CMOS facet has a distinct focal plane; hence the full system is a multiple aperture camera. An FPGA based solution is proposed for real time application deployment. The omnidirectional vision r

Approximating a signal or an image with a sparse linear expansion from an overcomplete dictionary of atoms is an extremely useful tool to solve many signal processing problems. Finding the sparsest approximation of a signal from an arbitrary dictionary is a NP-hard problem. Despite of this, several algorithms have been proposed that provide sub-optimal solutions. However, it is generally difficult to know how close the computed solution is to being ``optimal'', and whether another algorithm could provide a

A novel compressive sampling scheme suitable for highly scalable hardware implementations is presented. The prototype design is implemented in a 0.18um standard CMOS technology and utilizes compressed acquisition to boost the overall power efficiency. Spec ...

In this work we explore the potentialities of a representational framework based on Matching Pursuit (MP) for the decomposition of audio-visual signals over redundant dictionaries. It is relatively easy for a human to correctly interpret a scene consisting on a combination of acoustic and visual stimuli and to take profit of both the information to experience a richer perception of the world. On the contrary, computer systems have considerable difficulties when having to deal with multimodal signals, and th

In this work, we study the effect of inserting spatially local temporal adaptivity to motion compensated frame adaptive transforms for video coding. Motion compensation aligns the temporal wavelet decomposition along motion trajectories. However, valid trajectories for an efficient multi-scale filtering have a finite duration in time. This is due to well known effects like occlusions or inaccurate motion estimation. Hence, the signal encountered by the temporal wavelet transform can be seen as a piecewise-s

A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT), Speed-up Robust Feature (SU ...

This paper explores a novel approach for ventricular and atrial activities estimation in electrocardiogram (ECG) signals, based on sparse source separation. Sparse decompositions of ECG over signal-adapted multi-component dictionaries can lead to natural s ...

A Nordtvedt effect at cosmological scales affects the acoustic oscillations imprinted in the cosmic microwave background. The gravitational baryonic mass density of the universe is inferred at the first peak scale from WMAP data. The independent determination of the inertial baryonic mass density through the measurement of the deuterium abundance in the framework of standard big bang nucleosynthesis leads to a new constraint on a possible violation of the strong equivalence principle at the recombination ti

In general purpose computer vision systems, unsupervised image analysis is mandatory in order to achieve an automatic operation. In this paper a different approach to image segmentation for natural scenes is presented. Scale-Space representation is used to extract the structure from meaningful objects in the image. Two different scale-spaces are analysed in the paper. On one hand Isotropic Diffusion (linear scale-space) is presented as the basis for an uncommitted front end, not relying on any special featu

Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral graph neural networks, reflecting two largely separate research traditions in machine learning and signal processing. This paper argues that this divide is mostly artificial, hindering progress in the field. We propose a viewpoint in which both MPNNs and spectral GNNs are understood as different parametrizations of permutation-equivariant operators acting on graph signals. From this perspective, many

High-quality light field photography has been one of the most difficult challenges in computational photography. Conventional methods either sacrifice resolution, use multiple devices, or require multiple images to be captured. Combining coded image acquis ...

Peptides and proteins are biomolecules that exist in a broad spectrum of size, structure, and function. Both structure and function are defined by the underlying sequence of amino acids, causing the polyamide to take three-dimensional conformations when in solution. Despite significant efforts and advances in function and conformation prediction, there remains a critical need for computational methods to accurately infer protein function from sequence and structure. Recent advancements in Graph Neural Netwo

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first

k

eigenvectors of the similarity matrix' Laplacian, whose computati ...

A fast algorithm is developed for the directional correlation of scalar band-limited signals and band-limited steerable filters on the sphere. The asymptotic complexity associated to it through simple quadrature is of order O(L^5), where 2L stands for the ...

This paper presents a novel approach to acquire multichannel wireless intracranial neural data based on a compressive sensing scheme. The designed circuits are extremely compact and low-power which confirms the relevance of the proposed approach for multic ...

Vision is a natural tool for human-computer interaction, since it pro- vides visual feedback to the user and mimics some human behaviors. It requires however the fast and robust computation of motion primi- tives, which remains a difficult problem. In this ...

Decoder-Transformers have achieved remarkable success and have laid the groundwork for the development of Large Language Models (LLMs). At the core of these models is the self-attention matrix, which allows different tokens to interact with each other. This process is remarkably similar to the message-passing mechanism used in Graph Neural Networks (GNNs), and as such decoder-Transformers suffer many of the optimization difficulties studied extensively in the GNN literature. In this paper, we present a unif

This report studies the effect of introducing a priori knowledge to recover sparse representations when overcomplete dictionaries are used. We focus mainly on Greedy algorithms and Basis Pursuit as for our algorithmic basement, while a priori is incorporated by suitably weighting the elements of the dictionary. A unique sufficient condition is provided under which Orthogonal Matching Pursuit, Matching Pursuit and Basis Pursuit are able to recover the optimally sparse representation of a signal when a priori

Compressed Sensing (CS) is a new acquisition- compression paradigm for low-complexity energy-aware sensing and compression. By merging both sampling and compression, CS is very promising to develop practical ultra-low power read- out systems for wireless b ...

The dictionary approach to signal and image processing has been massively investigated in the last two decades, proving very attractive for a wide range of applications. The effectiveness of dictionary-based methods, however, is strongly influenced by the choice of the set of basis functions. Moreover the structure of the dictionary is of paramount importance regarding efficient implementation and practical applications such as image coding. In this work, an overcomplete code for sparse representation ofnat

Pierre Vandergheynst

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A Real-time Multi-camera System with Omnidirectional Image Reconstruction Capability

A simple test to check the optimality of a sparse signal approximation

Power-Efficient CMOS Image Acquisition System based on Compressive Sampling

Multimodal Analysis Using Redundant Parametric Decompositions

Intra-Adaptive Motion-Compensated Lifted Wavelets for Video Coding

FREAK: Fast Retina Keypoint

Ventricular and Atrial Activity Estimation Through Sparse ECG Signal Decompositions

Primordial constraint on the spatial dependence of the Newton constant

Segmentation of Natural Images Using Scale-Space Representations: A Linear and a Non-Linear Approach

Position: Message-passing and spectral GNNs are two sides of the same coin

Tensor low-rank and sparse light field photography

Graph rewiring for long range-aware protein learning

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Fast directional correlation on the sphere with steerable filters

Compressive Multichannel Cortical Signal Recording

FAST TV-L1 OPTICAL FLOW FOR INTERACTIVITY

Bridging Graph Neural Networks and Large Language Models: A Survey and Unified Perspective

On the Use of A Priori Information for Sparse Signal Representations

Power-Efficient Joint Compressed Sensing of Multi-Lead ECG Signals

Learning structured dictionaries for image representation