Arthur Ardeshir Goshtasby

Theory and Applications of Image Registration

• Gebundenes Buch

Produktbeschreibung

A hands-on guide to image registration theory and methods--with examples of a wide range of real-world applications

Theory and Applications of Image Registration offers comprehensive coverage of feature-based image registration methods. It provides in-depth exploration of an array of fundamental issues, including image orientation detection, similarity measures, feature extraction methods, and elastic transformation functions. Also covered are robust parameter estimation, validation methods, multi-temporal and multi-modality image registration, methods for determining the orientation of an image, methods for identifying locally unique neighborhoods in an image, methods for detecting lines in an image, methods for finding corresponding points and corresponding lines in images, registration of video images to create panoramas, and much more.

Theory and Applications of Image Registration provides readers with a practical guide to the theory and underpinning principles. Throughout the book numerous real-world examples are given, illustrating how image registration can be applied to problems in various fields, including biomedicine, remote sensing, and computer vision. Also provided are software routines to help readers develop their image registration skills. Many of the algorithms described in the book have been implemented, and the software packages are made available to the readers of the book on a companion website. In addition, the book:
_ Explores the fundamentals of image registration and provides a comprehensive look at its multi-disciplinary applications
_ Reviews real-world applications of image registration in the fields of biomedical imaging, remote sensing, computer vision, and more
_ Discusses methods in the registration of long videos in target tracking and 3-D reconstruction
_ Addresses key research topics and explores potential solutions to a number of open problems in image registration
_ Includes a companion website featuring fully implemented algorithms and image registration software for hands-on learning

Theory and Applications of Image Registration is a valuable resource for researchers and professionals working in industry and government agencies where image registration techniques are routinely employed. It is also an excellent supplementary text for graduate students in computer science, electrical engineering, software engineering, and medical physics.

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Produktdetails

• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119171710
• 1. Auflage
• Seitenzahl: 528
• Erscheinungstermin: 21. August 2017
• Englisch
• Abmessung: 231mm x 157mm x 30mm
• Gewicht: 960g
• ISBN-13: 9781119171713
• ISBN-10: 1119171717
• Artikelnr.: 44557157

Autorenporträt

Arthur Ardeshir Goshtasby, PhD, is a professor in the Department of Computer Science and Engineering at Wright State University. Dr. Goshtasby has more than thirty years of experience in the areas of computer vision and pattern recognition and has published more than sixty journal articles and seven book chapters, addressing issues in image registration. He is the author of 2-D and 3-D Image Registration (Wiley, 2005).

Inhaltsangabe

Contributors xv

Acknowledgments xvii

About the Companion Website xix

1 Introduction 1

1.1 Organization of the Book 3

1.2 Further Reading 5

References 5

2 Image Orientation Detection 9

2.1 Introduction 9

2.2 Geometric Gradient and Geometric Smoothing 13

2.2.1 Calculating Geometric Gradients 15

2.3 Comparison of Geometric Gradients and Intensity Gradients 18

2.4 Finding the Rotational Difference between Two Images 21

2.5 Performance Evaluation 23

2.5.1 Reliability 23

2.5.2 Accuracy 31

2.5.3 Computational Complexity 32

2.6 Registering Images with a Known Rotational Difference 34

2.7 Discussion 36

2.8 Further Reading 37

References 40

3 Feature Point Detection 43

3.1 Introduction 43

3.2 Variant Features 44

3.2.1 Central Moments 44

3.2.2 Uniqueness 48

3.3 Invariant Features 50

3.3.1 Rotation-Invariant Features 50

3.3.1.1 Laplacian of Gaussian (LoG) Detector 51

3.3.1.2 Entropy 53

3.3.1.3 InvariantMoments 55

3.3.2 SIFT: A Scale-and Rotation-Invariant Point Detector 58

3.3.3 Radiometric-Invariant Features 60

3.3.3.1 Harris Corner Detector 60

3.3.3.2 Hessian Corner Detector 63

3.4 Performance Evaluation 64

3.5 Further Reading 68

References 68

4 FeatureLineDetection 75

4.1 Hough Transform Using Polar Equation of Lines 79

4.2 Hough Transform Using Slope and y-Intercept Equation of Lines 82

4.3 Line Detection Using Parametric Equation of Lines 86

4.4 Line Detection by Clustering 89

4.5 Line Detection by Contour Tracing 92

4.6 Line Detection by Curve Fitting 95

4.7 Line Detection by Region Subdivision 101

4.8 Comparison of the Line Detection Algorithms 106

4.8.1 Sensitivity to Noise 106

4.8.2 Positional and Directional Errors 106

4.8.3 Length Accuracy 109

4.8.4 Speed 109

4.8.5 Quality of Detected Lines 109

4.9 Revisiting Image Dominant Orientation Detection 117

4.10 Further Reading 121

References 125

5 Finding Homologous Points 133

5.1 Introduction 133

5.2 Point Pattern Matching 134

5.2.1 Parameter Estimation by Clustering 137

5.2.2 Parameter Estimation by RANSAC 141

5.3 Point Descriptors 146

5.3.1 Histogram-Based Descriptors 147

5.3.2 SIFT Descriptor 148

5.3.3 GLOH Descriptor 151

5.3.4 Composite Descriptors 152

5.3.4.1 Hu InvariantMoments 152

5.3.4.2 Complex Moments 152

5.3.4.3 Cornerness Measures 153

5.3.4.4 Power Spectrum Features 154

5.3.4.5 Differential Features 155

5.3.4.6 Spatial Domain Features 155

5.4 SimilarityMeasures 160

5.4.1 Correlation Coefficient 160

5.4.2 Minimum Ratio 161

5.4.3 Spearman's 161

5.4.4 Ordinal Measure 162

5.4.5 Correlation Ratio 162

5.4.6 Shannon Mutual Information 164

5.4.7 Tsallis Mutual Information 165

5.4.8 F-Information 166

5.5 Distance Measures 167

5.5.1 Sum of Absolute Differences 167

5.5.2 Median of Absolute Differences 167

5.5.3 Square Euclidean Distance 168

5.5.4 Intensity-Ratio Variance 168

5.5.5 Rank Distance 169

5.5.6 Shannon Joint Entropy 169

5.5.7 Exclusive F-Information 170