Publications by Pascal Frossard

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With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspec ...

Understanding the organizational principles of human brain activity at the systems level remains a major challenge in network neuroscience. Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patt ...

Research in multimedia is always investigating new ways of improving the immersive experience of the users. One current solution consists in designing systems which offer a high level of interactivity, such as multiview content navigation where the point o ...

Augmented reality, interactive navigation in 3D scenes, multiview video, and other emerging multimedia applications require large sets of images, hence larger data volumes and increased resources compared with traditional video services. The significant in ...

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals. Defining signals on graphs relaxes such complexities by representing the evolution of signals over a space using ...

Enabling users to interactively navigate through different viewpoints of a static scene is a new interesting functionality in 3D streaming systems. While it opens exciting perspectives towards rich multimedia applications, it requires the design of novel r ...

In this paper we discuss the problem of alignment of patterns under arbitrary rotation. When a generic image pattern is geometrically transformed, it typically spans a (possibly nonlinear) manifold in a high dimensional space. When the pattern of interest ...

In this paper, we deal with the problem of jointly determining the optimal coding strategy and the scheduling decisions when receivers obtain layered data from multiple servers. The layered data is encoded by means of prioritized random linear coding (PRLC ...

We consider two capacity problems in P2P networks. In the first one, the nodes have an infinite amount of data to send and the goal is to optimally allocate their uplink bandwidths such that the demands of every peer in terms of receiving data rate are met. We solve this problem through a mapping from a node-weighted graph featuring two labels per node to a max flow problem on an edge-weighted bipartite graph. In the second problem under consideration, the resource allocation is driven by the availability o

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames are similar, motion estimation is key to effective compression of th ...

Distributed graph signal processing algorithms require the network nodes to communicate by exchanging messages in order to achieve a common objective. These messages have a finite precision in realistic networks, which may necessitate to implement message ...

In this paper, we consider decoding of loss tolerant data encoded by network coding and transmitted over error-prone networks. Intermediate network nodes typically perform the random linear network coding in a Galois field and a Gaussian elimination is use ...

This paper addresses the problem of streaming packetized media over a lossy packet network, with sender-driven (re)transmission using acknowledgement feedback. The different transmission scenarios associated to a group of interdependent media data units are abstracted in terms of a finite alphabet of policies, for each single data unit. A rate-distortion optimized markovian framework is proposed, which supports the use of comprehensive feedback information. Contrarily to previous works in rate-distortion op

Transformation manifolds are quite attractive for image analysis applications that require transformation invariance properties. The geometric structure of a transformation manifold has a profound influence on the design of processing algorithm, and the cu ...

Network coding has been proposed recently as an efficient method to increase network throughput by allowing network nodes to combine packets instead of simply forwarding them. However, packet combinations in the network may increase delay, complexity and e ...

Depth sensing is essential for intelligent computer vision applications, but it often suffers from low range precision and spatial resolution. To address this problem, we propose a novel framework that combines non-uniform sampling and reconstruction based ...

This paper proposes a joint reconstruction algorithm for compressed correlated images that are given under the form of linear measurements. We first propose a geometry based model in order to describe the correlation between visual information in pairs of ...

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is the ...

Relationships between entities in datasets are often of multiple nature, like geographical distance, social relationships, or common interests among people in a social network, for example. This information can naturally be modeled by a set of weighted and ...

Pascal Frossard

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iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

Dynamics of functional network organization through graph mixture learning

Interactive multiview video system with low decoding complexity

Reference view selection in DIBR-based multiview coding

node2coords: Graph Representation Learning with Wasserstein Barycenters

Traffic Signal Prediction on Transportation Networks Using Spatio-Temporal Correlations on Graphs

Navigation domain representation for interactive multiview imaging

Image alignment with rotation manifolds built on sparse geometric expansions

Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery from Multiple Servers

A note on the data-driven capacity of P2P networks

Graph-based compression of dynamic 3D point cloud sequences

Optimized Quantization in Distributed Graph Signal Filtering

Approximate Decoding for Network Coded Inter-dependent Data

Packetized Media Streaming with Comprehensive Exploitation of Feedback Information

Curvature Analysis of Pattern Transformation Manifolds

Network coding node placement for delay minimization in streaming overlays

High-Quality Reconstruction of Depth Maps From Graph-Based Non-Uniform Sampling

Joint reconstruction of correlated images from compressed linear measurements

Multi-task additive models with shared transfer functions based on dictionary learning

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds