• Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing See large image

    Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing (Hardback) By (author) Michael Elad

    $61.13 - Save $25.77 29% off - RRP $86.90 Free delivery worldwide Available
    Dispatched in 3 business days
    When will my order arrive?
    Add to basket | Add to wishlist |

    DescriptionA 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.


Other books

Other books in this category
Showing items 1 to 11 of 11

 

Reviews | Bibliographic data
  • Full bibliographic data for Sparse and Redundant Representations

    Title
    Sparse and Redundant Representations
    Subtitle
    From Theory to Applications in Signal and Image Processing
    Authors and contributors
    By (author) Michael Elad
    Physical properties
    Format: Hardback
    Number of pages: 396
    Width: 155 mm
    Height: 235 mm
    Thickness: 25 mm
    Weight: 826 g
    Language
    English
    ISBN
    ISBN 13: 9781441970107
    ISBN 10: 144197010X
    Classifications

    BIC E4L: MAT
    Nielsen BookScan Product Class 3: S7.8
    B&T Book Type: NF
    LC subject heading:
    B&T Modifier: Region of Publication: 01
    BIC subject category V2: UG
    B&T Merchandise Category: SCI
    B&T General Subject: 710
    B&T Modifier: Academic Level: 02
    LC classification: QA
    Ingram Subject Code: MA
    B&T Modifier: Text Format: 06
    DC22: 515
    Abridged Dewey: 515
    BIC subject category V2: UYS
    BISAC V2.8: MAT017000
    LC subject heading:
    BIC subject category V2: PBWH, PBKJ
    Warengruppen-Systematik des deutschen Buchhandels: 16270
    BISAC V2.8: MAT034000, MAT003000
    LC subject heading:
    LC classification: TK5102.9
    DC22: 515.3
    Libri: BILD2150, BILE2800, GRAF6508
    BIC subject category V2: PBU
    LC subject heading:
    Libri: OPTI6000, APPR6000, NAEH4000, P0041829, RECG5754
    LC classification: QA1-939, TA342-343, TA1637-1638, QA299.6-433, QA402.5-402.6, QA401-425
    Thema V1.0: UG, UYS, PBKJ, PBWH, PBU
    Illustrations note
    120 black & white illustrations, 41 colour illustrations, biography
    Publisher
    Springer-Verlag New York Inc.
    Imprint name
    Springer-Verlag New York Inc.
    Publication date
    02 October 2010
    Publication City/Country
    New York, NY
    Author Information
    Michael Elad has been working at The Technion in Haifa, Israel, since 2003 and is currently an Associate Professor. He is one of the leaders in the field of sparse representations. He does prolific research in mathematical signal processing with more than 60 publications in top ranked journals. He is very well recognized and respected in the scientific community.
    Review quote
    From the reviews: "This book approaches sparse and redundant representations from an engineering perspective and emphasizes their use as a signal modeling tool and their application in image and signal processing. ... This book is well suited to practitioners in the signals and image processing community ... . The public availability of the source code used in the numerical experiments throughout the book could help students make the transition from theory to practice and allow them to get hands-on experience with the inner workings of the various algorithms."--- (Ewout van den Berg, SIAM Review, Vol. 53 (4), 2011) "The concept of sparse representations for signals and images is explored in the book under review. ... The book offers an important and organized view of this field, setting the foundations of the future research. ... The presented book is written to serve as the material for an advanced one-semester graduate course for engineering students. It will be of interest for all specialists working in the area of sparse and redundant representations application in signal and image processing." (Tzvetan Semerdjiev, Zentralblatt MATH, Vol. 1211, 2011)
    Back cover copy
    The field of sparse and redundant representation modeling has gone through a major revolution in the past two decades. This started with a series of algorithms for approximating the sparsest solutions of linear systems of equations, later to be followed by surprising theoretical results that guarantee these algorithms performance. With these contributions in place, major barriers in making this model practical and applicable were removed, and sparsity and redundancy became central, leading to state-of-the-art results in various disciplines. One of the main beneficiaries of this progress is the field of image processing, where this model has been shown to lead to unprecedented performance in various applications. This book provides a comprehensive view of the topic of sparse and redundant representation modeling, and its use in signal and image processing. It offers a systematic and ordered exposure to the theoretical foundations of this data model, the numerical aspects of the involved algorithms, and the signal and image processing applications that benefit from these advancements. The book is well-written, presenting clearly the flow of the ideas that brought this field of research to its current achievements. It avoids a succession of theorems and proofs by providing an informal description of the analysis goals and building this way the path to the proofs. The applications described help the reader to better understand advanced and up-to-date concepts in signal and image processing. Written as a text-book for a graduate course for engineering students, this book can also be used as an easy entry point for readers interested in stepping into this field, and for others already active in this area that are interested in expanding their understanding and knowledge. "The book is accompanied by a Matlab software package that reproduces most of the results demonstrated in the book. A link to the free software is available on springer.com.""
    Table of contents
    Preface.- Part I. Theoretical and Numerical Foundations.- 1. Introduction.- 2. Uniqueness and Uncertainty.- 3. Pursuit Algorithms - Practice.- 4. Pursuit Algorithms - Guarantees.- 5. From Exact to Approximate Solution.- 6. Iterated Shrinkage Algorithms.- 7.Towards Average Performance Analysis.- 8. The Danzig Selector Algorithm.- Part II. Signal and Image Processing Applications.- 9. Sparsity-Seeking Methods in Signal Processing.- 10. Image Deblurring - A Case Study.- 11. MAP versus MMSE Estimation.- 12. The Quest For a Dictionary.- 13. Image Compression - Facial Images.- 14. Image Denoising.- 15. Other Applications.- 16. Concluding Remarks.- Bibliography.- Index