2-D and 3-D image registration for medical, remote sensing, and industrial applications医疗、遥感与工业应用的二维与三维图像配准 下载 网盘 kindle mobi 115盘 pdf pdb rtf

To master the fundamentals of image registration, there is no more comprehensive source than 2-D and 3-D Image Registration. In addition to delving into the relevant theories of image registration, the author presents their underlying algorithms. You'll also discover cutting-edge techniques to use in remote sensing, industrial, and medical applications. Examples of image registration are presented throughout, and the companion Web site contains all the images used in the book and provides links to software and algorithms discussed in the text, allowing you to reproduce the results in the text and develop images for your own research needs. 2-D and 3-D Image Registration serves as an excellent textbook for classes in image registration as well as an invaluable working resource.

作者简介：

A. ARDESHIR GOSHTASBY, PHD, is a professor in the department of computer science and engineering at Wright State University. Dr. Goshtasby has been developing solutions to image registration problems since 1983 and has numerous publications to his credit.

Preface

Acknowledgments

Acronyms

1 Introduction

　1.1 Terminologies

　1.2 Steps in Image Registration

　1.3 Summary of the Chapters to Follow

　1.4 Bibliographical Remarks

2 Preprocessing

2.1 Image Enhancement

　　2.1.1 Image smoothing

　　2.1.2 Deblurring

　2.2 Image Segmentation

　　2.2.1 Intensity thresholding

　　2.2.2 Boundary detection

　2.3 Summary

　2.4 Bibliographical Remarks

3 Feature Selection

　3.1 Points

　3.2 Lines

　　3.2.1 Line detection using the Hough transform

　　3.2.2 Least-squares line fitting

　　3.2.3 Line detection using image gradients

　3.3 Regions

　3.4 Templates

　3.5 Summary

　3.6 Bibliographical Remarks

4 Feature Correspondence

　4.1 Point Pattern Matching

　　4.1.1 Matching using scene coherence

　　4.1.2 Matching using clustering

　　4.1.3 Matching using invariance

　4.2 Line Matching

　4.3 Region Matching

　　4.3.1 Shape matching

　　4.3.2 Region matching by relaxation labeling

　4.4 Chamfer Matching

　　4.4.1 Distance transform

　4.5 Template Matching

　　4.5.1 Similarity measures

　　4.5.2 Gaussian-weighted templates

　　4.5.3 Template size

　　4.5.4 Coarse-to-fine methods

　4.6 Summary

　4.7 Bibliographical Remarks

5 Transformation Functions

　5.1 Similarity Transformation

　……

6 Resampling

7 Performance Evaluation

8 Image Fusion

9 Image Mosaicking

10 Stereo Depth perception

Glossary

References

Index

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