Concept

Oxford English Dictionary

Related publications (28)

Characterization of Real-World Power System Signals in Non-Stationary Conditions using a Dictionary Approach

Mario Paolone, Asja Derviskadic, Guglielmo Frigo, Alexandra Cameron Karpilow

As power grids transition towards low-inertia net-works based on converter-interfaced renewable energy resources, they become increasingly vulnerable to extreme dynamics. Currently, the most advanced methods for signal processing in power systems are embed ...

IEEE2021

Convex optimization in sums of Banach spaces

Michaël Unser, Shayan Aziznejad

We characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements. The underlying hypothesis is that the solution is decomposable as a finite sum of compo ...

2021

Concept Discovery for The Interpretation of Landscape Scenicness

Devis Tuia

In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic concepts ...

2020

Deep Micro-Dictionary Learning and Coding Network

Yan Yan, Wei Wang, Wei Xiao

In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling, fully, connected, input/output, etc.) but the main difference is that the fundamental convolutional l ...

IEEE2019

Image Based Relighting of Cultural Artifacts

Niranjan Thanikachalam

By incorporating computational methods into the image acquisition pipeline, computational photography has opened up new avenues in the representation and visualization of real world objects in the digital world. For example, we can sample a scene under a f ...

EPFL2016

Transformation-Invariant Dictionary Learning for Classification with 1-Sparse Representations

Pascal Frossard, Elif Vural, Ahmet Caner Yüzügüler

Sparse representations of images in well-designed dictionaries can be used for effective classification. Meanwhile, training data available in most realistic settings are likely to be exposed to geometric transformations, which poses a challenge for the de ...

2014

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

A convex optimization framework for global tractography

Jean-Philippe Thiran, Alessandro Daducci, Alia Lemkaddem

In this article we present a novel approach for diffusion MRI global tractography. Our formulation models the signal in each voxel as a linear combination of fiber-tract basis func- tions, which consist of a comprehensive set of plausible fiber tracts that ...

2013

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

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Pascal Frossard, Pierre Vandergheynst, Adel Rahmoune

This paper introduces a novel algorithm for sparse approximation in redundant dictionaries called the M-term pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal compon ...

Institute of Electrical and Electronics Engineers2012

Dictionary learning in stereo imaging

Pascal Frossard, Ivana Tosic

This paper presents a new method for learning overcomplete dictionaries adapted to efficient joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms o ...

Institute of Electrical and Electronics Engineers2011

Ultrasound Tomography with Learned Dictionaries

Martin Vetterli, Pascal Frossard, Ivana Jovanovic, Ivana Tosic

We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scan- ners. Given the measurements, our algorithm finds a sparse image representation in an overcomplete dictionary that is adapted t ...

2010

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

Karin Schnass, Rémi Gribonval

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

\ell_1

-minimisation. The problem can also be seen as factorising a

\ddim \times \nsig

matrix $Y=(y_1 \ldots y_\nsig), , y_n\in \R^\ ...

Institute of Electrical and Electronics Engineers2010

Distributed multi-view image coding with learned dictionaries

Pascal Frossard, Ivana Tosic

This paper addresses the problem of distributed image coding in camera neworks. The correlation between multiple images of a scene captured from different viewpoints can be effiiciently modeled by local geometric transforms of prominent images features. Su ...

2009

Spread spectrum for imaging techniques in radio interferometry

Pierre Vandergheynst, Yves Wiaux, Gilles Puy

We consider the probe of astrophysical signals through radio interferometers with small field of view and baselines with non-negligible and constant component in the pointing direction. In this context, the visibilities measured essentially identify with a ...

Wiley-Blackwell2009

Sparsity & [and] dictionaries - algorithms & [and] design

Karin Schnass

With the flood of information available today the question how to deal with high dimensional data/signals, which are cumbersome to handle, to calculate with and to store, is highly important. One approach to reducing this flood is to find sparse signal rep ...

EPFL2009

Learning bimodal structure in audio-visual data

Pierre Vandergheynst, Gianluca Monaci

A novel model is presented to learn bimodally informative structures from audio-visual signals. The signal is represented as a sparse sum of audio- visual kernels. Each kernel is a bimodal function consisting of synchronous snippets of an audio waveform an ...

2009

Compressed sensing and redundant dictionaries

Pierre Vandergheynst, Karin Schnass

This article extends the concept of it compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, which is a composition of a random matrix of certain type and a deterministic ...

2008

Multichannel thresholding with sensing dictionaries

Pierre Vandergheynst, Karin Schnass, Rémi Gribonval

This paper shows introduces the use sensing dictionaries for p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We do both a worst case and average case recovery analyses of this ...

2007

Average Performance Analysis for Thresholding

Pierre Vandergheynst, Karin Schnass

In this article is shown that with high probability the thresholding algorithm can recover signals that are sparse in a redundant dictionary as long as the {\it 2-Babel function} is growing slowly. This implies that it can succeed for sparsity levels up to ...

IEEE Institute of Electrical and Electronics Engineers2007