Theory and Applications of Image Registration

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.

Contents in Brief

Dedication

Contributors

Acknowledgments

About the companion website

Chapter 1: Introduction

1.1 Organization of the Book

1.2 Further Reading

References

Chapter 2: Image Orientation Detection

2.1 Introduction

2.2 Geometric Gradient and Geometric Smoothing

2.3 Comparison of Geometric Gradients and Intensity Gradients

2.4 Finding the Rotational Difference between Two Images

2.5 Performance Evaluation

2.6 Registering Images with a Know Rotational Difference

2.7 Discussion

2.8 Further Reading

References

Chapter 3: Feature Point Detection

3.1 Introduction

3.2 Variant Features

3.3 Invariant Features

3.4 Performance Evaluation

3.5 Further Reading

References

Chapter 4: Feature Line Detection

4.1 Hough Transform Using Polar Equation of Lines

4.2 Hough Transform Using Slope and y–intercept Equation of Lines

4.3 Line Detection Using Parametric Equation of Lines

4.4 Line Detection by Clustering

4.5 Line Detection by Contour Tracing

4.6 Line Detection by Curve Fitting

4.7 Line Detection by Region Subdivision

4.8 Comparison of the Line Detection Algorithms

4.9 Revisiting Image Dominant Orientation Detection

4.10 Further Reading

References

Chapter 5: Finding Homologous Points

5.1 Introduction

5.2 Point Pattern Matching

5.3 Point Descriptors

5.4 Similarity Measures

5.5 Distance Measures

5.6 Template Matching

5.7 Robust Parameter Estimation

5.8 Finding Optimal Transformation Parameters

5.9 Performance Evaluation

5.10 Further Reading

References

Chapter 6: Finding Homologous Lines

6.1 Introduction

6.2 Determining Transformation Parameters from Line Parameters

6.3 Finding Homologous Lines by Clustering

6.4 Finding Homologous Lines by RANSAC

6.5 Line Grouping Using Local Image Information

6.6 Line Grouping Using Vanishing Points

6.7 Robust Parameter Estimation Using Homologous Lines

6.8 Revisiting Image Dominant Orientation Detection

6.9 Further Reading

References

Chapter 7: Nonrigid Image Registration

7.1 Introduction

7.2 Finding Homologous Points

7.3 Outlier Removal

7.4 Elastic Transformation Models

7.5 Further Reading

References

Chapter 8: Volume Image Registration

8.1 Introduction

8.2 Feature Point Detection

8.3 Finding Homologous Points

8.4 Transformation Models for Volume Image Registration

8.5 Performance Evaluation

8.6 Further Reading

References

Chapter 9: Validation Methods

9.1 Introduction

9.2 Validation Using Simulation Data

9.3 Validation Using a Gold Standard

9.4 Validation by an Expert Observer

9.5 Validation Using a Consistency Measure

9.6 Validation Using a Similarity/Distance Measure

9.7 Further Reading

References

Chapter 10: Video Image Registration

Edgardo Molina, Wai Lun Khoo, Hao Tang, Zhigang Zhu

10.1 Introduction

10.2 Motion Modeling

10.3 Image Alignment

10.4 Image Composition

10.5 Application Examples

10.6 Further Reading

References

Chapter 11: Multitemporal Image Registration

11.1 Introduction

11.2 Finding Transformation Parameters from Line Parameters

11.3 Finding an Initial Set of Homologous Lines

11.4 Maximizing the Number of Homologous Lines

11.5 Examples of Multitemporal Image Registration

11.6 Further Reading

References

Chapter 12: Open Problems and Research Topics

12.1 Finding Rotational Difference between Multimodality Images

12.2 Designing a Robust Image Descriptor

12.3 Finding Homologous Lines for Nonrigid Registration

12.4 Nonrigid Registration Using Homologous Lines

12.5 Transformation Models with Nonsymmetric Basis Functions

12.6 Finding Homologous Points along Homologous Contours

12.7 4–D Image Registration

References

A Image Registration Software

A.1 Chapter 2: Image Orientation Detection

A.2 Chapter 3: Feature Point Detection

A.3 Chapter 4: Feature Line Detection

A.4 Chapter 5: Finding Homologous Points

A.5 Chapter 6: Finding Homologous Lines

A.6 Chapter 7: Nonrigid Image Registration

A.7 Chapter 8: Volume Image Registration

References

Glossary

Acronyms

List of Symbols

Index

Arthur A. 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).