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Pierre Vandergheynst

Cette personne a quitté l’EPFL

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Publications associées (214)

Page 3 sur 11

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

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Pierre Vandergheynst, Xavier Bresson, Michaël Defferrard

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or wor ...

2016

Towards Stationary Time-Vertex Signal Processing

Pierre Vandergheynst, Nathanaël Perraudin, Andreas Loukas, Francesco Grassi

Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks. Yet, though these systems are often dynamic, state-of-the-art methods ...

Ieee2017

Processor Enhancements for Media Streaming Applications

Pierre Vandergheynst, Eric Debes, Sébastien Bilavarn

The development of more processing demanding applications on the Internet (video broadcasting) on one hand and the popularity of recent devices at the user level (digital cameras, wireless videophones, ...) on the other hand introduce challenges at several ...

Springer, Kluwer Academic Publishers2005

Audio-Visual Object Extraction using Graph Cuts

Pierre Vandergheynst, Anna Llagostera Casanovas

We propose a novel method to automatically extract the audio-visual objects that are present in a scene. First, the synchrony between related events in audio and video channels is exploited to identify the possible locations of the sound sources. Video reg ...

Institute of Electrical and Electronics Engineers2012

Light Field Tensor Recovery

Pierre Vandergheynst, Mahdad Hosseini Kamal

This paper presents a novel approach to capture light field in camera arrays based on the tensor recovery framework. Light fields are acquired by a linear array of cameras with overlapping field of view. We represent the light fields in shape of a tensor t ...

2012

Plenoptic based super-resolution for omnidirectional image sequences

Pascal Frossard, Pierre Vandergheynst, Luigi Bagnato

This paper addresses the reconstruction of high resolution omnidirectional images from a low resolution video acquired by an omnidirectional camera moving in a static scene. In order to exploit the additional information provided by the side images in the ...

2010

The continuous wavelet transform on conic sections

Pierre Vandergheynst, Iva Bogdanova Vandergheynst

We review the known construction of the continuous wavelet transform (CWT) on the two-sphere. Next we describe the construction of a CWT on the upper sheet of a two- sheeted hyperboloid, emphasizing the similarities between the two cases. Finally we give s ...

2008

Non-Gaussianity analysis on local morphological measures of WMAP data

Pierre Vandergheynst, Yves Wiaux

The decomposition of a signal on the sphere with the steerable wavelet constructed from the second Gaussian derivative gives access to the orientation, signed-intensity and elongation of the signal’s local features. In the present work, the non-Gaussianity ...

2008

Graph-based denoising for time-varying point clouds

Yann Mikaël Schoenenberger, Pierre Vandergheynst, Johann Paratte

Noisy 3D point clouds arise in many applications. They may be due to errors when creating a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information betw ...

2015

Power-Efficient CMOS Image Acquisition System based on Compressive Sampling

Yusuf Leblebici, Pierre Vandergheynst, Alexandre Schmid, Mahdad Hosseini Kamal, Nikola Katic, Mustafa Kilic

A novel compressive sampling scheme suitable for highly scalable hardware implementations is presented. The prototype design is implemented in a 0.18um standard CMOS technology and utilizes compressed acquisition to boost the overall power efficiency. Spec ...

IEEE2013

FREAK: Fast Retina Keypoint

Pierre Vandergheynst, Alexandre Massoud Alahi, Raphaël Ortiz

A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT), Speed-up Robust Feature (SU ...

Ieee2012

Ventricular and Atrial Activity Estimation Through Sparse ECG Signal Decompositions

Pierre Vandergheynst, Jean-Marc Vesin, Mathieu Lemay, Lorenzo Granai, Oscar Divorra Escoda

This paper explores a novel approach for ventricular and atrial activities estimation in electrocardiogram (ECG) signals, based on sparse source separation. Sparse decompositions of ECG over signal-adapted multi-component dictionaries can lead to natural s ...

2006

Compressive Multichannel Cortical Signal Recording

Yusuf Leblebici, Pierre Vandergheynst, Alexandre Schmid, Mahsa Shoaran, Mahdad Hosseini Kamal

This paper presents a novel approach to acquire multichannel wireless intracranial neural data based on a compressive sensing scheme. The designed circuits are extremely compact and low-power which confirms the relevance of the proposed approach for multic ...

IEEE2013

Tensor low-rank and sparse light field photography

Pierre Vandergheynst, Mahdad Hosseini Kamal

High-quality light field photography has been one of the most difficult challenges in computational photography. Conventional methods either sacrifice resolution, use multiple devices, or require multiple images to be captured. Combining coded image acquis ...

Elsevier2016

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first

k

eigenvectors of the similarity matrix' Laplacian, whose computati ...

Ieee2016

Fast directional correlation on the sphere with steerable filters

Pierre Vandergheynst, Yves Wiaux, Laurent Jacques

A fast algorithm is developed for the directional correlation of scalar band-limited signals and band-limited steerable filters on the sphere. The asymptotic complexity associated to it through simple quadrature is of order O(L^5), where 2L stands for the ...

2006

Power-Efficient Joint Compressed Sensing of Multi-Lead ECG Signals

David Atienza Alonso, Pierre Vandergheynst, Giovanni Ansaloni, Hossein Mamaghanian

Compressed Sensing (CS) is a new acquisition- compression paradigm for low-complexity energy-aware sensing and compression. By merging both sampling and compression, CS is very promising to develop practical ultra-low power read- out systems for wireless b ...

IEEE Press2014

FAST TV-L1 OPTICAL FLOW FOR INTERACTIVITY

Pierre Vandergheynst, Gilles Puy, Johann Paratte, Emmanuel D'Angelo

Vision is a natural tool for human-computer interaction, since it pro- vides visual feedback to the user and mimics some human behaviors. It requires however the fast and robust computation of motion primi- tives, which remains a difficult problem. In this ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames

Pierre Vandergheynst, David Shuman

We consider the problem of designing spectral graph filters for the construction of dictionaries of atoms that can be used to efficiently represent signals residing on weighted graphs. While the filters used in previous spectral graph wavelet constructions ...

Institute of Electrical and Electronics Engineers2015