Publications de Pascal Frossard

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Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for ...

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which c ...

This paper presents a framework for coarse scene geometry estimation, based on sparse representations of omnidirectional images with geometrical basis functions. We introduce a correlation model that relates sparse components in different views with local ...

We consider the problem of distributed classification of multiple observations of the same object that are collected in an ad hoc network of vision sensors. Assuming that each sensor captures a different observation of the same object, the problem is to cl ...

State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations. One of the most effective strategies to improve robustness is adversarial training. In this paper, we investigate the effect of adversarial training on the geometry of the classification landscape and decision boundaries. We show in particular that adversarial training leads to a significant decrease in the curvature of the loss surface with respect to inputs, leading to a drastically more "linear" behaviour

The accuracy of the side information (SI) is critical in the performance of distributed video coding algorithms. The SI is typically built at a decoder based on the reconstructed data and on channel coding parity bits transmitted by the encoder. The optima ...

This paper presents a new image representation method based on anisotropic refinement. It has been shown that wavelets are not optimal to code 2-D objects which need true 2-D dictionaries for efficient approximation. We propose to use rotations and anisotropic scaling to build a real bi-dimensional dictionary. Matching Pursuit then stands as a natural candidate to provide an image representation with an anisotropic refinement scheme. It basically decomposes the image as a series of basis functions weighted

Omnidirectional images are spherical signals captured by cameras with 360-degree field of view. In order to be compressed using existing encoders, these signals are mapped to planar domain. A commonly used planar representation is the equirectangular one, ...

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a prevalence of human made structures, which in most of the cases, are either ...

State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. Despite the importance of this p ...

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 ...

This paper addresses the problem of efficient representation of scenes captured by distributed omnidirectional vision sensors. We propose a novel geometric model to describe the correlation between different views of a 3-D scene. We first approximate the c ...

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

The performance of automated classification algorithms for medical images needs to be very high and especially robust in order to be adopted into healthcare. Most of the time the main challenge is unregistered data, since it is usually captured: 1) from di ...

Emerging applications in multiview streaming look for providing interactive navigation services to video players. The user can ask for information from any viewpoint with a minimum transmission delay. The purpose is to provide user with as much information ...

Modern media data such as 360 degrees videos and light field (LF) images are typically captured in much higher dimensions than the observers' visual displays. To efficiently browse high-dimensional media, a navigational streaming model is considered: a cli ...

Graph neural networks have emerged as a popular and powerful tool for learning hierarchical representation of graph data. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of data in graph neu ...

This paper addresses the problem of optimal rate allocation for multiple description coding with redundant signal expansions. In case of redundant descriptions, the quantization of the transform coefficients has clearly to be adapted to the importance of t ...

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

Pascal Frossard

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Knowledge Distillation with Graph Neural Networks for Epileptic Seizure Detection

Modurec: Recommender Systems with Feature and Time Modulation

Coarse scene geometry estimation from sparse approximations of multi-view omnidirectional images

Distributed classification of multiple observation sets by consensus

Robustness via curvature regularization, and vice versa

EXIT-based Side Information Refinement in Wyner-Ziv Video Coding

Efficient image representation by anisotropic refinement in Matching Pursuit

Rate Distortion Optimized Graph Partitioning for Omnidirectional Image Coding

Joint Graph-based Depth Refinement and Normal Estimation

DeepFool: a simple and accurate method to fool deep neural networks

Graph Signal Processing: Overview, Challenges and Applications

Geometry-Based Distributed Scene Representation With Omnidirectional Vision Sensors

Graph Laplacian Mixture Model

Robustness via curvature regularization, and vice versa

Automated Eardrum Registration From Light-Field Data

Extended Layered Depth Image Representation in Multiview Navigation

Landmarking for Navigational Streaming of Stored High-Dimensional Media

Multiscale Representation Learning of Graph Data With Node Affinity

Multiple description image coding with redundant expansions and optimal quantization

Adaptive Entropy-Constrained Matching Pursuit Quantization