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Pascal Frossard

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Related publications (428)

Robustness of Classifiers to Universal Perturbations: A Geometric Perspective

Pascal Frossard, Alhussein Fawzi, Seyed Mohsen Moosavi Dezfooli

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we provide a quantitative analysi ...

2018

Bulged Eardrum Detection From 3D Data

Pascal Frossard, Ivana Tosic, Sofia Karygianni

Bulging is a medical characteristic of the eardrum that is crucial for the diagnosis of acute otitis media. This work proposes a novel classification method for distinguishing bulged eardrums from non-bulged ones. The method uses novel key features extract ...

IEEE2018

Automated Eardrum Registration From Light-Field Data

Pascal Frossard, Ivana Tosic, Sofia Karygianni

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 ...

IEEE2018

Geometric robustness of deep networks: analysis and improvement

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Can Kanbak

Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance properties of deep networks to such transformations. We propose ManiFool as a simpl ...

2018

Geometry-Consistent Light Field Super-Resolution via Graph-Based Regularization

Pascal Frossard, Mattia Rossi

Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus, to depth estimation and image-based rendering. However ...

2018

Dynamic Adaptive Streaming for Multi-Viewpoint Omnidirectional Videos

Pascal Frossard, Francesca De Simone, Xavier Corbillon

Full immersion inside a Virtual Reality (VR) scene requires six Degrees of Freedom (6DoF) applications where the user is allowed to perform translational and rotational movements within the virtual space. The implementation of 6DoF applications is however ...

ASSOC COMPUTING MACHINERY2018

QoE Driven Mobile Edge Caching Placement for Adaptive Video Streaming

Pascal Frossard, Laura Toni, Chenglin Li

2018

Graph Signal Processing: Overview, Challenges and Applications

Pascal Frossard, Pierre Vandergheynst

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 ...

2018

A graph learning approach for light field image compression

Pascal Frossard, Touradj Ebrahimi, Hermina Petric Maretic, Irene Viola

In recent years, light field imaging has attracted the attention of the academic and industrial communities thanks to its enhanced rendering capabilities that allow to visualise contents in a more immersive and interactive way. However, those enhanced capa ...

Spie-Int Soc Optical Engineering2018

Learning of robust spectral graph dictionaries for distributed processing

Pascal Frossard, Ntorina Thanou

We consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries. We ...

SPRINGEROPEN2018

Optimized Data Representation for Interactive Multiview Navigation

Pascal Frossard, Thomas Maugey, Rui Ma

In contrary to traditional media streaming services where a unique media content is delivered to different users, interactive multiview navigation applications enable users to choose their own viewpoints and freely navigate in a three-dimensional scene. Th ...

2018

Empirical study of the topology and geometry of deep networks

Pascal Frossard, Alhussein Fawzi, Seyed Mohsen Moosavi Dezfooli

The goal of this paper is to analyze the geometric properties of deep neural network image classifiers in the input space. We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary. ...

2018

Rate Distortion Optimized Graph Partitioning for Omnidirectional Image Coding

Pascal Frossard, Francesca De Simone, Thomas Maugey, Mira Rizkallah

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, ...

IEEE COMPUTER SOC2018

Universal adversarial perturbations

Pascal Frossard, Alhussein Fawzi, Seyed Mohsen Moosavi Dezfooli

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for co ...

Ieee2017

Graph-Based Light Field Super-Resolution

Pascal Frossard, Mattia Rossi

Light field cameras can capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post capture refocus, to depth estimation and image-based rendering. How ...

2017

Optimizing Landmark Insertions for Interactive Light Field Streaming

Pascal Frossard, Yuan Yuan

Light field imaging enables a user to navigate and observe a static 3D scene from different viewpoints. Downloading the entire data prior to navigation would incur a large startup delay. Instead, previous works propose an interactive light field streaming ...

2017

Learning sparse models of diffusive graph signals

Pascal Frossard, Ntorina Thanou, Shuyu Dong

2017

Graph-Based Classification of Omnidirectional Images

Pascal Frossard, Renata Khasanova

Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view. Their images are often processed with classical methods, which might unfortunately lead to non-optimal solutions as these methods ar ...

2017

Deformable block based motion estimation in omnidirectional image sequences

Pascal Frossard, Francesca De Simone

This paper presents an extension of block-based motion estimation for omnidirectional videos, based on a camera and translational object motion model that accounts for the spherical geometry of the imaging system. We use this model to design a new algorith ...

2017

Finite Length Performance of Random MAC Strategies

Pascal Frossard, Laura Toni, Konstantinos Dovelos

2017

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