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Ntorina Thanou

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Publications associées (39)

Learning Graphs From Data: A Signal Representation Perspective

Pascal Frossard, Ntorina Thanou, Xiaowen Dong

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

Methods and apparatuses for encoding and decoding digital images or video streams

Pascal Frossard, Ntorina Thanou, Giulia Fracastoro

The invention relates to a method and an apparatus for encoding and/or decoding digital images, wherein said encoding apparatus (1100) comprises processing means (1110) configured for determining weights of a graph related to an image by minimizing a cost ...

2018

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

Learning sparse models of diffusive graph signals

Pascal Frossard, Ntorina Thanou, Shuyu Dong

2017

Graph learning under sparsity priors

Pascal Frossard, Ntorina Thanou, Hermina Petric Maretic

Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. ...

Ieee2017

Learning time varying graphs

Pascal Frossard, Ntorina Thanou, Vasilis Kalofolias, Andreas Loukas

We consider the problem of inferring the hidden structure of high-dimensional dynamic systems from the perspective of graph learning. In particular, we aim at capturing the dynamic relationships between nodes by a sequence of graphs. Our approach is motiva ...

Ieee2017

Graph Signal Processing : Sparse Representation and Applications

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature ...

EPFL2016

Graph Transform Learning for Image Compression

Pascal Frossard, Ntorina Thanou, Giulia Fracastoro

2016

Graph-based compression of dynamic 3D point cloud sequences

Pascal Frossard, Ntorina Thanou

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 ...

Institute of Electrical and Electronics Engineers2016

Learning Laplacian Matrix in Smooth Graph Signal Representations

Pascal Frossard, Pierre Vandergheynst, Ntorina Thanou, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not alwa ...

Institute of Electrical and Electronics Engineers2016

Distributed Signal Processing with Graph Spectral Dictionaries

Pascal Frossard, Ntorina Thanou

2015

Laplacian Matrix Learning for Smooth Graph Signal Representation

Pascal Frossard, Pierre Vandergheynst, Ntorina Thanou, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the emerging field of signal processing on graphs. In this paper, we address the problem of learning graph Laplacians, which is similar to learning graph topologies, such that the input data fo ...

2015

Graph-based motion estimation and compression for dynamic 3D point cloud compression

Pascal Frossard, Ntorina Thanou

2015

Learning Parametric Dictionaries for Signals on Graphs

Pascal Frossard, Ntorina Thanou, David Shuman

In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties – the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on wei ...

Institute of Electrical and Electronics Engineers2014

Parametric dictionary learning for graph signals

Pascal Frossard, Ntorina Thanou, David Shuman

We propose a parametric dictionary learning algorithm to design structured dictionaries that sparsely represent graph signals. We incorporate the graph structure by forcing the learned dictionaries to be concatenations of subdictionaries that are polynomia ...

2013

Distributed average consensus with quantization refinement

Pascal Frossard, Effrosyni Kokiopoulou, Ntorina Thanou, Ye Pu

We consider the problem of distributed average consensus in a sensor network where sensors exchange quantized information with their neighbors. We propose a novel quantization scheme that exploits the increasing correlation between the values exchanged by ...

Institute of Electrical and Electronics Engineers2013

Progressive quantization in distributed average consensus

Pascal Frossard, Effrosyni Kokiopoulou, Ntorina Thanou

We consider the problem of distributed average consensus in a sensor network where sensors exchange quantized information with their neighbors. In particular, we exploit the increasing correlation between the exchanged values throughout the iterations of t ...

2012

Compressed classification of observation sets with linear subspace embeddings

Pascal Frossard, Ntorina Thanou

We consider the problem of classification of a pattern from multiple compressed observations that are collected in a sensor network. In particular, we exploit the properties of random projections in generic sensor devices and we take some first steps in in ...

2011

Polynomial Filter Design for Quantized Consensus

Pascal Frossard, Effrosyni Kokiopoulou, Ntorina Thanou, Hyung Gon Park

We consider the problem of distributed average consensus where sensors exchange quantized data with their neighbors. We deploy a polynomial filtering approach in the network nodes in order to accelerate the convergence of the consensus problem. The quantiz ...

2010

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