Sparse Image and Signal Processing

Sparse Image and Signal Processing

This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median ...

Author: Jean-Luc Starck

Publisher: Cambridge University Press

ISBN: 9781139642835

Category: Computers

Page:

View: 903

This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics. A final chapter explores a paradigm shift in signal processing, showing that previous limits to information sampling and extraction can be overcome in very significant ways. Matlab and IDL code accompany these methods and applications to reproduce the experiments and illustrate the reasoning and methodology of the research are available for download at the associated web site.
Categories: Computers

Image and Signal Processing

Image and Signal Processing

This book constitutes the refereed proceedings of the 8th International Conference on Image and Signal Processing, ICISP 2018, held in Cherbourg, France, in July 2018.

Author: Alamin Mansouri

Publisher: Springer

ISBN: 9783319942117

Category: Computers

Page: 547

View: 430

This book constitutes the refereed proceedings of the 8th International Conference on Image and Signal Processing, ICISP 2018, held in Cherbourg, France, in July 2018. The 58 revised full papers were carefully reviewed and selected from 122 submissions. The contributions report on the latest developments in image and signal processing, video processing, computer vision, multimedia and computer graphics, and mathematical imaging and vision.
Categories: Computers

Sparse and Redundant Representations

Sparse and Redundant Representations

This book provides a comprehensive view of the topic of sparse and redundant representation modeling, and its use in signal and image processing.

Author: Michael Elad

Publisher: Springer Science & Business Media

ISBN: 9781441970114

Category: Mathematics

Page: 376

View: 237

A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham’s razor: “Entities should not be multiplied without neces sity. ” This principle enabled scientists to select the ”best” physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage“spoken”whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the “language” or “dictionary” used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you’ll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.
Categories: Mathematics

A Wavelet Tour of Signal Processing

A Wavelet Tour of Signal Processing

Features: * Balances presentation of the mathematics with applications to signal processing * Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox New in this edition * Sparse signal representations in dictionaries ...

Author: Stephane Mallat

Publisher: Academic Press

ISBN: 0080922023

Category: Technology & Engineering

Page: 832

View: 657

Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford University The new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications. Features: * Balances presentation of the mathematics with applications to signal processing * Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox New in this edition * Sparse signal representations in dictionaries * Compressive sensing, super-resolution and source separation * Geometric image processing with curvelets and bandlets * Wavelets for computer graphics with lifting on surfaces * Time-frequency audio processing and denoising * Image compression with JPEG-2000 * New and updated exercises A Wavelet Tour of Signal Processing: The Sparse Way, Third Edition, is an invaluable resource for researchers and R&D engineers wishing to apply the theory in fields such as image processing, video processing and compression, bio-sensing, medical imaging, machine vision and communications engineering. Stephane Mallat is Professor in Applied Mathematics at École Polytechnique, Paris, France. From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University, and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company. Includes all the latest developments since the book was published in 1999, including its application to JPEG 2000 and MPEG-4 Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolbox Balances presentation of the mathematics with applications to signal processing
Categories: Technology & Engineering

Document Image Processing

Document Image Processing

Sparse Image Representation Recently, sparse representation emerged as a
powerful tool for efficient representation and ... The underlying assumption of
methods based on sparse representation is that signals such as audio and
images are ...

Author: Ergina Kavallieratou

Publisher: MDPI

ISBN: 9783038971054

Category: Technology & Engineering

Page: 216

View: 973

This book is a printed edition of the Special Issue "Document Image Processing" that was published in J. Imaging
Categories: Technology & Engineering

Image and Signal Processing for Remote Sensing

Image and Signal Processing for Remote Sensing

The solution given by an SVM is intrisically sparse . In our case study , we
needed at least 40 % of training samples ( i . e . SVs ) in order to obtain good
results . In the case of Reg - RBFNN , sparsity is imposed by selecting the most
suitable ...

Author:

Publisher:

ISBN: UOM:39015058328041

Category: Image processing

Page:

View: 814

Categories: Image processing

Signal and Image Processing for Remote Sensing Second Edition

Signal and Image Processing for Remote Sensing  Second Edition

This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches.

Author: C.H. Chen

Publisher: CRC Press

ISBN: 9781439855966

Category: Technology & Engineering

Page: 619

View: 807

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience. This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing. The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing. New in This Edition The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include: Compressive sensing The mixed pixel problem with hyperspectral images Hyperspectral image (HSI) target detection and classification based on sparse representation An ISAR technique for refocusing moving targets in SAR images Empirical mode decomposition for signal processing Feature extraction for classification of remote sensing signals and images Active learning methods in classification of remote sensing images Signal subspace identification of hyperspectral data Wavelet-based multi/hyperspectral image restoration and fusion The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing (CRC Press 2006).
Categories: Technology & Engineering

Image Understanding Using Sparse Representations

Image Understanding Using Sparse Representations

The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications.

Author: Jayaraman J. Thiagarajan

Publisher: Morgan & Claypool Publishers

ISBN: 9781627053600

Category: Technology & Engineering

Page: 118

View: 475

Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.
Categories: Technology & Engineering

Hybrid Image and Signal Processing

Hybrid Image and Signal Processing

The nonzero entries of the sparse matrices Q ( k , l ) are given by ( cf . ( 4 ) ) : ( k ,
1 ) qiu eti ! ) - elk , 1 ) 1 sisi sno , 15 < i < no , 91 , ( 5 ) ( k , l ) - eno + 1 , no + 1 + i -
j ! , 1 ) ( k , 1 ) Eno + 1 - i + j , no + 1 ) ( 4 , 1 ) eno + 1 + i , no + 1 + j eno + 1 , no + ...

Author:

Publisher:

ISBN: UOM:39015048213212

Category: Image processing

Page:

View: 918

Categories: Image processing

Hybrid Image and Signal Processing

Hybrid Image and Signal Processing

The excellent speedup of algorithms on sparse images follows from noticing that
for sparse images the running times are dominated by the internal processing
time to find the possible extreme points , which is O ( M - / N ) . So as the number
of ...

Author: David Paul Casasent

Publisher: Society of Photo Optical

ISBN: UCSD:31822003191129

Category: Electronic book

Page: 259

View: 784

Categories: Electronic book

Signal Processing for Remote Sensing

Signal Processing for Remote Sensing

This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches.

Author: C.H. Chen

Publisher: CRC Press

ISBN: MINN:31951D02657105Q

Category: Technology & Engineering

Page: 265

View: 993

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience. This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing. The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing. New in This Edition The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include: Compressive sensing The mixed pixel problem with hyperspectral images Hyperspectral image (HSI) target detection and classification based on sparse representation An ISAR technique for refocusing moving targets in SAR images Empirical mode decomposition for signal processing Feature extraction for classification of remote sensing signals and images Active learning methods in classification of remote sensing images Signal subspace identification of hyperspectral data Wavelet-based multi/hyperspectral image restoration and fusion The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing (CRC Press 2006).
Categories: Technology & Engineering

Advances in Neural Information Processing Systems 19

Advances in Neural Information Processing Systems 19

In this paper, application of sparse representation (factorization) of signals over
an overcomplete basis (dictionary) for signal classification is discussed. ... On the
other hand, discriminative methods, such as linear discriminative analysis (LDA),
are better suited for classification tasks. ... [5]; the applications of the sparse
representation for different tasks, such as signal separation, denoising, coding,
image ...

Author: Bernhard Schölkopf

Publisher: MIT Press

ISBN: 9780262195683

Category: Computers

Page: 1643

View: 332

The annual conference on NIPS is the flagship conference on neural computation. It draws top academic researchers from around the world & is considered to be a showcase conference for new developments in network algorithms & architectures. This volume contains all of the papers presented at NIPS 2006.
Categories: Computers

Statistical methods for image and signal processing

Statistical methods for image and signal processing

2.5 Discussion We have shown that a wavelet basis and a mixture prior
composed of a Dirac delta function and a Gaussian can be adapted to natural
images resulting in very sparse image representations. The resulting learned
basis is ...

Author: Philip Andrew Sallee

Publisher:

ISBN: UCAL:X68608

Category:

Page: 262

View: 259

Categories:

Digital Signal Processing with Matlab Examples Volume 3

Digital Signal Processing with Matlab Examples  Volume 3

This is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs.

Author: Jose Maria Giron-Sierra

Publisher: Springer

ISBN: 9789811025402

Category: Technology & Engineering

Page: 431

View: 660

This is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressed sensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.
Categories: Technology & Engineering

Image and Signal Processing for Networked E health Applications

Image and Signal Processing for Networked E health Applications

Terrestrial-based positioning may be more imprecise with sparse
deploymentofbasestationsinruralenvironments,wheresatellitevisibilityisbetter.
Position-fixing in indoor environments may also exploit other technologies such
as WLAN.

Author: Ilias G. Maglogiannis

Publisher: Morgan & Claypool Publishers

ISBN: 9781598290363

Category: Medical

Page: 95

View: 317

E-Health applications facilitate the exchange of information between clinicians or between institutions, reducing costs, extending the scope and reach of medical facilities, enhancing the quality of service offered on- and off-site, and provides new means of medical supervision and preemptive medicine. Currently, the integration of medical networking and medical information systems is treated as an obvious need; standalone medical networking environments are no longer a reality and the term "telemedicine" is in practice used interchangeably with e-Health.This book provides an overview of the field of Networked e-health applications and telemedicine and its supporting technologies. Chapters focus on signals, signal processing, electroencephalogram (EEG) and the Electrocardiogram (ECG or EKG), medical imaging, as well as a look at medical signal processing and classification from the point of view of urgent medical support, where not every possible type of medical equipment is readily available. Also covered is the encoding for transmission of medical data. Compression is of central importance, as is loss of information and ways to minimize it. The final section of the book addresses the design, implementation, and operation of e-Health systems.
Categories: Medical

Sparse Modeling for Image and Vision Processing

Sparse Modeling for Image and Vision Processing

More specifically, the work focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.

Author: Julien Mairal

Publisher: Now Publishers

ISBN: 1680830082

Category: Computers

Page: 216

View: 965

Sparse Modeling for Image and Vision Processing offers a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.
Categories: Computers

Mathematical Morphology and Its Applications to Image and Signal Processing

Mathematical Morphology and Its Applications to Image and Signal Processing

Our first experiment consists of finding a contour fitting sparse and ... We note that
a post-processing step for producing a smooth isosurface from such a binary
object reconstruction has recently been proposed by Lempitzky [22]. The TV ...

Author: Pierre Soille

Publisher: Springer

ISBN: 9783642215698

Category: Computers

Page: 484

View: 279

This book contains the refereed proceedings of the 10th International Symposium on Mathematical Morphology, ISMM 2011 held in Verbania-Intra, Italy in July 2011. It is a collection of 39 revised full papers, from which 27 were selected for oral and 12 for poster presentation, from a total of 49 submissions. Moreover, the book features two invited contributions in the fields of remote sensing, image analysis and scientific visualization. The papers are organized in thematic sections on theory, lattices and order, connectivity, image analysis, processing and segmentation, adaptive morphology, algorithms, remote sensing, visualization, and applications.
Categories: Computers

Signal Processing and Multimedia

Signal Processing and Multimedia

This paper presents a novel approach to deal with single image super-resolution.
It's proven that image patches can be represented as a sparse linear combination
of elements from a well-chosen over-complete dictionary. Using a dictionary of ...

Author: Sankar Kumar Pal

Publisher: Springer

ISBN: 9783642176418

Category: Computers

Page: 328

View: 353

Welcome to the proceedings of the 2010 International Conferences on Signal Proce- ing, Image Processing and Pattern Recognition (SIP 2010), and Multimedia, C- puter Graphics and Broadcasting (MulGraB 2010) – two of the partnering events of the Second International Mega-Conference on Future Generation Information Te- nology (FGIT 2010). SIP and MulGraB bring together researchers from academia and industry as well as practitioners to share ideas, problems and solutions relating to the multifaceted - pects of image, signal, and multimedia processing, including their links to compu- tional sciences, mathematics and information technology. In total, 1,630 papers were submitted to FGIT 2010 from 30 countries, which - cludes 225 papers submitted to SIP/MulGraB 2010. The submitted papers went through a rigorous reviewing process: 395 of the 1,630 papers were accepted for FGIT 2010, while 53 papers were accepted for SIP/MulGraB 2010. Of the 53 papers 8 were selected for the special FGIT 2010 volume published by Springer in the LNCS series. 37 papers are published in this volume, and 8 papers were withdrawn due to technical reasons. We would like to acknowledge the great effort of the SIP/MulGraB 2010 Inter- tional Advisory Boards and members of the International Program Committees, as well as all the organizations and individuals who supported the idea of publishing this volume of proceedings, including SERSC and Springer. Also, the success of these two conferences would not have been possible without the huge support from our sponsors and the work of the Chairs and Organizing Committee.
Categories: Computers

Sparse Representations for Radar with MATLAB Examples

Sparse Representations for Radar with MATLAB Examples

This book presents the basic mathematical concepts along with a number of useful MATLAB(R) examples to emphasize the practical implementations both inside and outside the radar field.

Author: Peter Knee

Publisher: Morgan & Claypool Publishers

ISBN: 9781627050340

Category: Technology & Engineering

Page: 71

View: 844

Although the field of sparse representations is relatively new, research activities in academic and industrial research labs are already producing encouraging results. The sparse signal or parameter model motivated several researchers and practitioners to explore high complexity/wide bandwidth applications such as Digital TV, MRI processing, and certain defense applications. The potential signal processing advancements in this area may influence radar technologies. This book presents the basic mathematical concepts along with a number of useful MATLAB(r) examples to emphasize the practical implementations both inside and outside the radar field.
Categories: Technology & Engineering

Hyperspectral Image Analysis

Hyperspectral Image Analysis

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding.

Author: Saurabh Prasad

Publisher: Springer Nature

ISBN: 9783030386177

Category: Computers

Page: 466

View: 346

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Categories: Computers