Publications by Pascal Frossard

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Interactive free viewpoint video offers the possibility for each user to independently choose the views of a 3D scene to be displayed at de- coder. The visual content is commonly represented by N texture and depth map pairs that capture different viewpoint ...

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 ...

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 ...

In this work, we borrow tools from the field of adversarial robustness, and propose a new framework that permits to relate dataset features to the distance of samples to the decision boundary. Using this framework we identify the subspace of features used by CNNs to classify large-scale vision benchmarks, and reveal some intriguing aspects of their robustness to distributions shift. Specifically, by manipulating the frequency content in CIFAR-10 we show that the existence of redundant features on a dataset

An important question in the design of interactive multiview systems consists in determining the information needed by the decoder for high quality navigation between the views. Most of the existing techniques focus on the captured sequences and only consi ...

We address the problem of joint optimal rate allocation and scheduling between media source rate and error protection rate in scalable streaming applications over lossy multipath networks. Starting from a distortion representation of the received media inf ...

In multi-view applications, multiple cameras acquire the same scene from different perspectives, which results in correlated video streams. It becomes important to exploit this correlation at the acquisition side (i.e., in the source coding) or at the rece ...

We address the problem of building a manifold in order to represent a set of geometrically transformed images by selecting a good common sparse approximation of them with parametric atoms. We propose a greedy method to construct a representative pattern su ...

In this paper, we propose the use of (adaptive) nonlinear approximation for dimensionality reduction. In particular, we propose a dimensionality reduction method for learning a parts based representation of signals using redundant dictionaries. A redundant ...

This paper proposes a methodology to estimate the correlation model between a pair of images that are given under the form of linear measurements. We consider an image pair whose common objects are relatively displaced due to the positioning of vision sens ...

Data augmentation is the process of generating samples by transforming training data, with the target of improving the accuracy and robustness of classifiers. In this paper, we propose a new automatic and adaptive algorithm for choosing the transformations ...

Network coding has been recently proposed as an efficient method to improve throughput, minimize delays and remove the need for reconciliation between network nodes in distributed streaming systems. It permits to take advantage of the path and node diversi ...

This paper deals with the problem of efficiently computing the optical flow of image sequences acquired by omnidirectional (nearly full field of view) cameras. We formulate the problem in the natural spherical geometry associated with these devices and ext ...

We present a performance analysis for image registration with gradient descent. We consider a typical multiscale registration setting where the global 2-D translation between a pair of images is estimated by smoothing the images and minimizing the distance ...

Multiple description coding offers an elegant and competitive solution for data transmission over lossy packetbased networks, with a graceful degradation in quality as losses increase. In the same time, coding techniques based on redundant transforms give ...

A method of providing SVC video content with added media content, comprising the steps of: receiving an original video content transmitted from a remote site in a bit stream and including original video encoding parameters decoding said original video enco ...

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 ...

Texture and depth maps of two neighboring camera viewpoints are typically required for synthesis of an intermediate virtual view via depth-image-based rendering (DIBR). However, the bitrate overhead required for reconstruction ofmultiple texture and depth ...

Omnidirectional images represent a special type of images that are captured by vision sensors with a 360-degree field of view. This work targets the compression of such images by taking into account their particular geometry. We first map omnidirectional i ...

In this paper, we propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We achieve this by combining two novel paradigms that have recently seen much success in a wide variety of real-world applications: graph neural networks (GNN), and meta-learning. We train a Meta-GNN based classifier that learns a global model from a set of training patients such that this global model can eventually be adapted to a new unseen patien

Pascal Frossard

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In-Network View Re-Sampling for Interactive Free Viewpoint Video Streaming

Universal adversarial perturbations

Graph-Based Classification of Omnidirectional Images

Redundant features can hurt robustness to distributions shift

Consistent view synthesis in interactive multiview imaging

Forward Error Correction for Multipath Media Streaming

Correlation-aware packet scheduling for multi-camera streaming

Learning pattern transformation manifolds with parametric atom selection

Dimensionality Reduction with Adaptive Approximation

Dense Disparity Estimation from Linear Measurements

Adaptive data augmentation for image classification

NC Node Selection Game in Collaborative Streaming Systems

Optical Flow and Depth from Motion for Omnidirectional Images using a TV-L1 Variational Framework on Graphs

Analysis of Descent-Based Image Registration

Multiple Description Coding with Redundant Expansions and Application to Image Communications

Method of providing scalable video coding (svc) video content with added media content

Learning of robust spectral graph dictionaries for distributed processing

RD optimized auxiliary information for inpainting-based view synthesis

Low bit-rate compression of omnidirectional images

A Meta-gnn Approach to Personalized Seizure Detection and Classification