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Sparse Representations and Compressive Sensing for Imaging and Vision

Part of the SpringerBriefs in Electrical and Computer Engineering Book Series

DOI: 10.1007/978-1-4614-6381-8

ISBN: 978-1-4614-6380-1

Publisher: Springer New York - Springer Journals

Jan 4, 2013

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Introduction

Patel, Vishal M.; Chellappa, Rama

Pages: 1 –2

[Compressive sampling [23, 47] is an emerging field that has attracted considerable interest in signal/image processing, computer vision and information theory. Recent advances in compressive sensing have led to the development of imaging devices that sense at measurement rates below than the...
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Compressive Sensing

Patel, Vishal M.; Chellappa, Rama

Pages: 3 –15

[Compressive sensing [47], [23] is a new concept in signal processing and information theory 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 number...
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Compressive Acquisition

Patel, Vishal M.; Chellappa, Rama

Pages: 17 –40

[Many imaging modalities have been proposed that make use of the theory of compressive sensing. In this chapter, we present several sensors designed using CS theory. In particular, we focus on the Single Pixel Camera (SPC) [54], [149], Magnetic Resonance Imaging (MRI) [79], [80], [108], Synthetic...
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Compressive Sensing for Vision

Patel, Vishal M.; Chellappa, Rama

Pages: 41 –61

[In this chapter, we present an overview of some of the recent works in computer vision and image understanding that make the use of compressive sampling and sparse representation. In particular, we show have CS and sparse representation have been used for various tracking algorithms. We then...
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Sparse Representation-based Object Recognition

Patel, Vishal M.; Chellappa, Rama

Pages: 63 –84

[In this chapter, we show how the sparse representation framework can be used to develop robust algorithms for object classification [156], [112], [106]. In particular, we will outline the Sparse Representation-based Classification (SRC) algorithm [156] and present its applications in robust...
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Dictionary Learning

Patel, Vishal M.; Chellappa, Rama

Pages: 85 –92

[Instead of using a pre-determined dictionary B, as in (5.1), one can directly learn it from the data [99]. Indeed, it has been observed that learning a dictionary directly from the training data rather than using a predetermined dictionary usually leads to better representation and hence can...
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Concluding Remarks

Patel, Vishal M.; Chellappa, Rama

Pages: 93 –94

[In this monograph, we have outlined some fundamental premises underlying the theories of sparse representation and compressed sensing. Based on these theories, we have examined several interesting imaging and vision applications such as magnetic resonance imaging, synthetic aperture radar...

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