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A Time-Vertex Signal Processing Framework

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Slepian Guided Filtering of Graph Signals

Dimitri Nestor Alice Van De Ville, Miljan Petrovic

Joint localization of graph signals in vertex and spectral domain is achieved in Slepian vectors calculated by either maximizing energy concentration (mu) or minimizing modified embedded distance (xi) in the subgraph of interest. On the other hand, graph L ...

SPIE-INT SOC OPTICAL ENGINEERING2019

In this work we consider the problem of learning an Erdos-Renyi graph over a diffusion network when: i) data from only a limited subset of nodes are available (partial observation); ii) and the inferential goal is to discover the graph of interconnections ...

IEEE2019

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Graph spectral analysis can yield meaningful embeddings of graphs by providing insight into distributed features not directly accessible in nodal domain. Recent efforts in graph signal processing have proposed new decompositions-for example, based on wavel ...

MIT PRESS2019

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus des ...

2019

Spherical Convolutionnal Neural Networks: Empirical Analysis of SCNNs

Frédérick Matthieu Gusset

Convolutional neural networks (CNNs) are powerful tools in Deep Learning mainly due to their ability to exploit the translational symmetry present in images, as they are equivariant to translations. Other datasets present different types of symmetries (e.g ...

2019

Topological Analog Signal Processing

Romain Christophe Rémy Fleury, Farzad Zangeneh Nejad

Analog signal processors have attracted a tremendous amount of attention recently, as they potentially offer much faster operation and lower power consumption than their digital versions. Yet, they are not preferable for large scale applications due to the ...

2019

Graph-Based Detection of Seams In 360-Degree Images

Pascal Frossard, Francesca De Simone, Sohyeong Kim, Roberto Gerson De Albuquerque Azevedo

In this paper, we propose an algorithm to detect a specific kind of distortions, referred to as seams, which commonly occur when a 360-degree image is represented in planar domain by projecting the sphere to a polyhedron, e.g, via the Cube Map (CM) project ...

2019

Global and Local Uncertainty Principles for Signals on Graphs

Pierre Vandergheynst, Nathanaël Perraudin, Benjamin Ricaud, David Shuman

Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluating linear signal transforms. Generalizations of such principles to the graph ...

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 signal filtering-based approach for detection of different edge types on airborne lidar data

Elif Vural, Eda Bayram

Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems ha ...

2017