By Vishal M. Patel

Compressed sensing or compressive sensing is a brand new notion in sign processing the place one measures a small variety of non-adaptive linear combos of the sign. those measurements tend to be a lot smaller than the variety of samples that outline the sign. From those small numbers of measurements, the sign is then reconstructed by way of non-linear method. Compressed sensing has lately emerged as a robust software for successfully processing information in non-traditional methods. during this publication, we spotlight a number of the key mathematical insights underlying sparse illustration and compressed sensing and illustrate the position of those theories in classical imaginative and prescient, imaging and biometrics problems.

Table of Contents

Cover

Sparse Representations and Compressive Sensing for Imaging and imaginative and prescient

ISBN 9781461463801 ISBN 9781461463818

Acknowledgements

Contents

Chapter 1 Introduction

1.1 Outline

Chapter 2 Compressive Sensing

2.1 Sparsity
2.2 Incoherent Sampling
2.3 Recovery
2.3.1 powerful CS
o 2.3.1.1 The Dantzig selector
2.3.2 CS restoration Algorithms
o 2.3.2.1 Iterative Thresholding Algorithms
o 2.3.2.2 grasping Pursuits
o 2.3.2.3 different Algorithms
2.4 Sensing Matrices
2.5 section Transition Diagrams
2.6 Numerical Examples

Chapter three Compressive Acquisition

3.1 unmarried Pixel Camera
3.2 Compressive Magnetic Resonance Imaging
3.2.1 picture Gradient Estimation
3.2.2 picture Reconstruction from Gradients
3.2.3 Numerical Examples
3.3 Compressive man made Aperture Radar Imaging
3.3.1 Slow-time Undersampling
3.3.2 picture Reconstruction
3.3.3 Numerical Examples
3.4 Compressive Passive Millimeter Wave Imaging
3.4.1 Millimeter Wave Imaging System
3.4.2 speeded up Imaging with prolonged Depth-of-Field
3.4.3 Experimental Results
3.5 Compressive gentle delivery Sensing

Chapter four Compressive Sensing for Vision

4.1 Compressive objective Tracking
4.1.1 Compressive Sensing for historical past Subtraction
4.1.2 Kalman Filtered Compressive Sensing
4.1.3 Joint Compressive Video Coding and Analysis
4.1.4 Compressive Sensing for Multi-View Tracking
4.1.5 Compressive Particle Filtering
4.2 Compressive Video Processing
4.2.1 Compressive Sensing for High-Speed Periodic Videos
4.2.2 Programmable Pixel Compressive Camerafor excessive pace Imaging
4.2.3 Compressive Acquisition of Dynamic Textures
o 4.2.3.1 Dynamic Textures and Linear Dynamical Systems
o 4.2.3.2 Compressive Acquisition of LDS
o 4.2.3.3 Experimental Results
4.3 form from Gradients
4.3.1 Sparse Gradient Integration
4.3.2 Numerical Examples

Chapter five Sparse Representation-based item Recognition

5.1 Sparse Representation
5.2 Sparse Representation-based Classification
5.2.1 powerful Biometrics Recognitionusing Sparse Representation
5.3 Non-linear Kernel Sparse Representation
5.3.1 Kernel Sparse Coding
5.3.2 Kernel Orthogonal Matching Pursuit
5.3.3 Kernel Simultaneous Orthogonal Matching Pursuit
5.3.4 Experimental Results
5.4 Multimodal Multivariate Sparse Representation
5.4.1 Multimodal Multivariate Sparse Representation
5.4.2 powerful Multimodal Multivariate Sparse Representation
5.4.3 Experimental Results
o 5.4.3.1 Preprocessing
o 5.4.3.2 function Extraction
o 5.4.3.3 Experimental Set-up
5.5 Kernel house Multimodal Recognition
5.5.1 Multivariate Kernel Sparse Representation
5.5.2 Composite Kernel Sparse Representation
5.5.3 Experimental Results

Chapter 6 Dictionary Learning

6.1 Dictionary studying Algorithms
6.2 Discriminative Dictionary Learning
6.3 Non-Linear Kernel Dictionary Learning

Chapter 7 Concluding Remarks

References