Sparse representations and compressive sensing for imaging and vision

✍ Scribed by Vishal M. Patel, Rama Chellappa (auth.)

• Publisher: Springer-Verlag New York
• Year: 2013
• Tongue: English
• Leaves: 111
• Series: SpringerBriefs in Electrical and Computer Engineering
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-x
Introduction....Pages 1-2
Compressive Sensing....Pages 3-15
Compressive Acquisition....Pages 17-40
Compressive Sensing for Vision....Pages 41-61
Sparse Representation-based Object Recognition....Pages 63-84
Dictionary Learning....Pages 85-92
Concluding Remarks....Pages 93-94
Back Matter....Pages 95-102

✦ Subjects

Signal, Image and Speech Processing; Image Processing and Computer Vision