Sparse Representations and Compressive Sensing for Imaging and Vision

  • Vishal M. Patel
  • Rama Chellappa

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Table of contents

  1. Front Matter
    Pages i-x
  2. Vishal M. Patel, Rama Chellappa
    Pages 1-2
  3. Vishal M. Patel, Rama Chellappa
    Pages 3-15
  4. Vishal M. Patel, Rama Chellappa
    Pages 17-40
  5. Vishal M. Patel, Rama Chellappa
    Pages 41-61
  6. Vishal M. Patel, Rama Chellappa
    Pages 63-84
  7. Vishal M. Patel, Rama Chellappa
    Pages 85-92
  8. Vishal M. Patel, Rama Chellappa
    Pages 93-94
  9. Back Matter
    Pages 95-102

About this book

Introduction

Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal.  These measurements are usually much smaller than the number of samples that define the signal.  From these small numbers of measurements, the signal is then reconstructed by non-linear procedure.  Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways.  In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.

Keywords

Biometrics Compressed Sensing Dictionary Learning Imaging Object Recognition Sparse Representation

Authors and affiliations

  • Vishal M. Patel
    • 1
  • Rama Chellappa
    • 2
  1. 1., Center for Automation ResearchUniversity of MarylandCollege ParkUSA
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-6381-8
  • Copyright Information The Author(s) 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Engineering
  • Print ISBN 978-1-4614-6380-1
  • Online ISBN 978-1-4614-6381-8
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • About this book