Sparse Representations and Compressive Sensing for Imaging and Vision

1. Front Matter

   Pages i-x

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2. Introduction

   • Vishal M. Patel, Rama Chellappa

   Pages 1-2

3. Compressive Sensing

   • Vishal M. Patel, Rama Chellappa

   Pages 3-15

4. Compressive Acquisition

   • Vishal M. Patel, Rama Chellappa

   Pages 17-40

5. Compressive Sensing for Vision

   • Vishal M. Patel, Rama Chellappa

   Pages 41-61

6. Sparse Representation-based Object Recognition

   • Vishal M. Patel, Rama Chellappa

   Pages 63-84

7. Dictionary Learning

   • Vishal M. Patel, Rama Chellappa

   Pages 85-92

8. Concluding Remarks

   • Vishal M. Patel, Rama Chellappa

   Pages 93-94

9. Back Matter

   Pages 95-102

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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.

• Biometrics
• Compressed Sensing
• Dictionary Learning
• Imaging
• Object Recognition
• Sparse Representation

• , Center for Automation Research, University of Maryland, College Park, USA

  Vishal M. Patel

• Center for Automation Research, University of Maryland, College Park, USA

  Rama Chellappa

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