The Experts below are selected from a list of 1632 Experts worldwide ranked by ideXlab platform

Pierre Vandergheynst - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Light field compressive sensing in camera arrays
    2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012
    Co-Authors: M. Hosseini Kamal, Mohammad Golbabaee, Pierre Vandergheynst
    Abstract:

    This paper presents a novel approach to capture light field in camera arrays based on the compressive sensing framework. Light fields are captured by a linear array of cameras with overlapping field of view. In this work, we design a Redundant Dictionary to exploit cross-cameras correlated structures to sparsely represent cameras image. Our main contributions are threefold. First, we exploit the correlations between the set of views by making use of a specially designed Redundant Dictionary. We show experimentally that the projection of complex scenes onto this Dictionary yields very sparse coefficients. Second, we propose an efficient compressive encoding scheme based on the random convolution framework [1]. Finally, we develop a joint sparse recovery algorithm for decoding the compressed measurements and show a marked improvement over independent decoding of CS measurements.

  • Light field compressive sensing in camera arrays
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2012
    Co-Authors: Mahdad Hosseini Kamal, Mohammad Golbabaee, Pierre Vandergheynst
    Abstract:

    This paper presents a novel approach to capture light field in cam- era arrays based on the compressive sensing framework. Light fields are captured by a linear array of cameras with overlapping field of view. In thiswork, we design a Redundant Dictionary to exploit cross- cameras correlated structures to sparsely represent cameras image. Our main contributions are threefold. First, we exploit the correla- tions between the set of views by making use of a specially designed Redundant Dictionary. We show experimentally that the projection of complex scenes onto this Dictionary yields very sparse coefficients. Second, we propose an efficient compressive encoding scheme based on the random convolution framework [1]. Finally, we develop a joint sparse recovery algorithm for decoding the compressed mea- surements and show a marked improvement over independent de- coding of CS measurements.

  • Compressed Sensing and Redundant Dictionaries
    IEEE Transactions on Information Theory, 2008
    Co-Authors: Holger Rauhut, Karin Schnass, Pierre Vandergheynst
    Abstract:

    This paper extends the concept of 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 Dictionary, has small restricted isometry constants. Thus, signals that are sparse with respect to the Dictionary can be recovered via basis pursuit (BP) from a small number of random measurements. Further, thresholding is investigated as recovery algorithm for compressed sensing, and conditions are provided that guarantee reconstruction with high probability. The different schemes are compared by numerical experiments.

  • MoTIF: An Efficient Algorithm for Learning Translation Invariant Dictionaries
    2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006
    Co-Authors: P. Jost, Pierre Vandergheynst, S. Lesage, R. Gribonval
    Abstract:

    The performance of approximation using Redundant expansions rely on having dictionaries adapted to the signals. In natural high-dimensional data, the statistical dependencies are, most of the time, not obvious. Learning fundamental patterns is an alternative to analytical design of bases and is nowadays a popular problem in the field of approximation theory. In many situations, the basis elements are shift invariant, thus the learning should try to find the best matching filters. We present a new algorithm for iteratively learning generating functions that can be shifted at all positions in the signal to generate a highly Redundant Dictionary

  • image compression using an edge adapted Redundant Dictionary and wavelets
    Signal Processing, 2006
    Co-Authors: Lorenzo Peotta, L Granai, Pierre Vandergheynst
    Abstract:

    Low bit rate image coding is an important problem regarding applications such as storage on low memory devices or streaming data on the internet. The state of the art in image compression is to use two-dimensional (2-D) wavelets. The advantages of wavelet bases lie in their multiscale nature and in their ability to sparsely represent functions that are piecewise smooth. Their main problem on the other hand, is that in 2-D wavelets are not able to deal with the natural geometry of images, i.e. they cannot sparsely represent objects that are smooth away from regular submanifolds. In this paper we propose an approach based on building a sparse representation of the edge part of images in a Redundant geometrically inspired library of functions, followed by suitable coding techniques. Best N-terms non-linear approximations in general dictionaries is, in most cases, a NP-hard problem and sub-optimal approaches have to be followed. In this work we use a greedy strategy, also known as Matching Pursuit to compute the expansion. The residual, that we suppose to be the smooth and texture part, is then coded using wavelets. A rate distortion optimization procedure chooses the number of functions from the Redundant Dictionary and the wavelet basis.

Xuefeng Chen - One of the best experts on this subject based on the ideXlab platform.

  • Incipient bearing fault diagnosis based on Redundant Dictionary pruning
    2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018
    Co-Authors: Zhibo Yang, Xuefeng Chen, Shaohua Tian
    Abstract:

    Incipient bearing fault detection has great significance for mechanical condition monitoring. One of the challenges is to extract the impulse features under high background noise. Recently, the sparse representation theory has made tremendous progress in removing noises and vibration feature extraction. The fault characteristics are adaptively extracted from the noisy vibration waveform by means of the Dictionary learning methods. However, under strong noise, the learned dictionaries contain a number of noise atoms, which influence the reconstruction performance of the impulse features. In order to address this problem, we propose a Redundant Dictionary pruning method to suppress the atom noise. It applies Lilliefors hypothesis test to judge noise atoms after the Dictionary learning procedure. The effectiveness and robustness of the proposed method are verified by the numerical simulations and the wind generator incipient bearing fault diagnosis.

  • I2MTC - Incipient bearing fault diagnosis based on Redundant Dictionary pruning
    2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018
    Co-Authors: Zhibo Yang, Xuefeng Chen, Shaohua Tian
    Abstract:

    Incipient bearing fault detection has great significance for mechanical condition monitoring. One of the challenges is to extract the impulse features under high background noise. Recently, the sparse representation theory has made tremendous progress in removing noises and vibration feature extraction. The fault characteristics are adaptively extracted from the noisy vibration waveform by means of the Dictionary learning methods. However, under strong noise, the learned dictionaries contain a number of noise atoms, which influence the reconstruction performance of the impulse features. In order to address this problem, we propose a Redundant Dictionary pruning method to suppress the atom noise. It applies Lilliefors hypothesis test to judge noise atoms after the Dictionary learning procedure. The effectiveness and robustness of the proposed method are verified by the numerical simulations and the wind generator incipient bearing fault diagnosis.

  • Sparse representation based on Redundant Dictionary and basis pursuit denoising for wind turbine gearbox fault diagnosis
    2016 International Symposium on Flexible Automation (ISFA), 2016
    Co-Authors: Boyuan Yang, Ruiping Li, Xuefeng Chen
    Abstract:

    Due to the dramatic growth of total installation and individual capacity make the failures of wind turbines costly or even unacceptable. Therefore, wind turbine fault diagnosis, which is considered as a useful tool to ensuring the safety of wind turbines and reducing costly system maintenances, is attracting increasing attention. In this paper, a novel fault diagnosis for wind turbine gearbox based on basis pursuit denoising (BPDN) and the union of Redundant Dictionary is proposed. The union of Redundant Dictionary is constructed based on the underlying prior information of vibrations signal with multicomponent coupling effect. Within the frame work of BPDN, sparse coefficient and corresponding time-frequency atoms can be obtained. By time-frequency representation of the reconstructed signal, fault information can be displayed. Finally, an engineering application of a wind turbine gearbox is used to verify the effectiveness of the proposed method.

  • Sparse Feature Identification Based on Union of Redundant Dictionary for Wind Turbine Gearbox Fault Diagnosis
    IEEE Transactions on Industrial Electronics, 2015
    Co-Authors: Zhaohui Du, Xuefeng Chen, Han Zhang
    Abstract:

    A primary challenge in fault diagnosis is to extract multiple components entangled within a noisy observation. Therefore, this paper describes and analyzes a novel framework, based on convex optimization, for simultaneously identifying multiple features from superimposed signals. This work adequately exploits the underlying prior information that multiple faults with similar frequency spectrum have different morphological waveforms that can be sparsely represented over the union of Redundant dictionaries. Within this framework, prior information is formulated into regularization terms, and a sparse optimization problem, which can be solved through the alternating direction method of multipliers (ADMM), is proposed. Meanwhile, the convergence and computational complexity of the proposed iterative framework are profoundly investigated. Moreover, sensitivity analyses and adaptive selection rules for the regularization parameters are described in detail through a set of comprehensive numerical studies. The proposed framework is validated through performing the diagnosis of multiple faults for gearbox in a wind farm. The comparison with respect to the state of the art in the field is illustrated in detail, which highlights the superiority of the proposed framework.

Pascal Frossard - One of the best experts on this subject based on the ideXlab platform.

  • Learning from sparse codes
    2016 IEEE International Conference on Image Processing (ICIP), 2016
    Co-Authors: Sofia Karygianni, Pascal Frossard
    Abstract:

    In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a Redundant Dictionary. We then formulate a new structure learning problem that learns molecules directly from image sparse codes, namely from the image representation in the atom domain. We build on a structural difference function that permits to compare molecules and we derive an algorithm that analyses sparse codes and estimates the most relevant signal structure without reconstructing the images. Experiments on both synthetic and real image datasets confirm the benefits of our new method compared to traditional learning methods.

  • Classification of unions of subspaces with sparse representations
    2013 Asilomar Conference on Signals Systems and Computers, 2013
    Co-Authors: Alhussein Fawzi, Pascal Frossard
    Abstract:

    We propose a preliminary investigation on the benefits and limitations of classifiers based on sparse representations. We specifically focus on the union of subspaces data model and examine binary classifiers built on a sparse non linear mapping (in a Redundant Dictionary) followed by a linear classifier. We study two common sparse non linear mappings (namely l0 and l1) and show that, in both cases, there exists a finite Dictionary such that the classifier discriminates the two classes correctly. This result paves the way towards a better understanding of the increasingly popular classifiers based on sparse representations, and provides initial insights on appropriate Dictionary design.

  • ICME - Dimensionality Reduction with Adaptive Approximation
    Multimedia and Expo 2007 IEEE International Conference on, 2007
    Co-Authors: Effrosyni Kokiopoulou, Pascal Frossard
    Abstract:

    In this paper, we propose the use of (adaptive) nonlinear approximation for dimensionality reduction. In particular, we propose a dimensionality reduction method for learning a parts based representation of signals using Redundant dictionaries. A Redundant Dictionary is an overcomplete set of basis vectors that spans the signal space. The signals are jointly represented in a common subspace extracted from the Redundant Dictionary, using greedy pursuit algorithms for simultaneous sparse approximation. The design of the Dictionary is flexible and enables the direct control on the shape and properties of the basis functions. Moreover, it allows to incorporate a priori and application-driven knowledge into the basis vectors, during the learning process. We apply our dimensionality reduction method to images and compare it with principal component analysis (PCA) and non-negative matrix factorization (NMF) and its variants, in the context of handwritten digit image recognition and face recognition. The experimental results suggest that the proposed dimensionality reduction algorithm is competitive to PCA and NMF and that it results into meaningful features with high discriminant value.

  • Dimensionality Reduction with Adaptive Approximation
    2007
    Co-Authors: Effrosyni Kokiopoulou, Pascal Frossard
    Abstract:

    In this paper, we propose the use of (adaptive) nonlinear approximation for dimensionality reduction. In particular, we propose a dimensionality reduction method for learning a parts based representation of signals using Redundant dictionaries. A Redundant Dictionary is an overcomplete set of basis vectors that spans the signal space. The signals are jointly represented in a common subspace extracted from the Redundant Dictionary, using greedy pursuit algorithms for simultaneous sparse approximation. The design of the Dictionary is flexible and enables the direct control on the shape and properties of the basis functions. Moreover, it allows to incorporate a priori and application-driven knowledge into the basis vectors, during the learning process. We apply our dimensionality reduction method to images and compare it with principal component analysis (PCA) and non-negative matrix factorization (NMF) and its variants, in the context of handwritten digit image recognition and face recognition. The experimental results suggest that the proposed dimensionality reduction algorithm is competitive to PCA and NMF and that it results into meaningful features with high discriminant value.

  • ICME - Distributed SVM Applied to Image Classification
    2006 IEEE International Conference on Multimedia and Expo, 2006
    Co-Authors: Effrosyni Kokiopoulou, Pascal Frossard
    Abstract:

    This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a Redundant Dictionary of atoms that spans the space of the observed signal. Redundant dictionaries allow for sparse representation of the observed signal, hence a good approximation of the support vector contributions, which is moreover robust to noise. The algorithm is applied to distributed image classification, in the context of handwritten digit recognition in a sensor network. The experimental results indicate that the proposed method is capable of achieving the same classification performance as the standard (non distributed) SVM, with an increased resiliency to noise.

Han Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Sparse Feature Identification Based on Union of Redundant Dictionary for Wind Turbine Gearbox Fault Diagnosis
    IEEE Transactions on Industrial Electronics, 2015
    Co-Authors: Zhaohui Du, Xuefeng Chen, Han Zhang
    Abstract:

    A primary challenge in fault diagnosis is to extract multiple components entangled within a noisy observation. Therefore, this paper describes and analyzes a novel framework, based on convex optimization, for simultaneously identifying multiple features from superimposed signals. This work adequately exploits the underlying prior information that multiple faults with similar frequency spectrum have different morphological waveforms that can be sparsely represented over the union of Redundant dictionaries. Within this framework, prior information is formulated into regularization terms, and a sparse optimization problem, which can be solved through the alternating direction method of multipliers (ADMM), is proposed. Meanwhile, the convergence and computational complexity of the proposed iterative framework are profoundly investigated. Moreover, sensitivity analyses and adaptive selection rules for the regularization parameters are described in detail through a set of comprehensive numerical studies. The proposed framework is validated through performing the diagnosis of multiple faults for gearbox in a wind farm. The comparison with respect to the state of the art in the field is illustrated in detail, which highlights the superiority of the proposed framework.

Zhaohui Du - One of the best experts on this subject based on the ideXlab platform.

  • Sparse Feature Identification Based on Union of Redundant Dictionary for Wind Turbine Gearbox Fault Diagnosis
    IEEE Transactions on Industrial Electronics, 2015
    Co-Authors: Zhaohui Du, Xuefeng Chen, Han Zhang
    Abstract:

    A primary challenge in fault diagnosis is to extract multiple components entangled within a noisy observation. Therefore, this paper describes and analyzes a novel framework, based on convex optimization, for simultaneously identifying multiple features from superimposed signals. This work adequately exploits the underlying prior information that multiple faults with similar frequency spectrum have different morphological waveforms that can be sparsely represented over the union of Redundant dictionaries. Within this framework, prior information is formulated into regularization terms, and a sparse optimization problem, which can be solved through the alternating direction method of multipliers (ADMM), is proposed. Meanwhile, the convergence and computational complexity of the proposed iterative framework are profoundly investigated. Moreover, sensitivity analyses and adaptive selection rules for the regularization parameters are described in detail through a set of comprehensive numerical studies. The proposed framework is validated through performing the diagnosis of multiple faults for gearbox in a wind farm. The comparison with respect to the state of the art in the field is illustrated in detail, which highlights the superiority of the proposed framework.