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E-raamat: Theory and Applications of Image Registration

(Wright State University)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 03-Jul-2017
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119171737
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Theory and Applications of Image RegistrationSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 03-Jul-2017
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119171737
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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.

Contributors xv
Acknowledgments xvii
About the Companion Website xix
1 Introduction 1(8)
1.1 Organization of the Book
3(2)
1.2 Further Reading
5(1)
References
5(4)
2 Image Orientation Detection 9(34)
2.1 Introduction
9(4)
2.2 Geometric Gradient and Geometric Smoothing
13(5)
2.2.1 Calculating Geometric Gradients
15(3)
2.3 Comparison of Geometric Gradients and Intensity Gradients
18(3)
2.4 Finding the Rotational Difference between Two Images
21(2)
2.5 Performance Evaluation
23(11)
2.5.1 Reliability
23(8)
2.5.2 Accuracy
31(1)
2.5.3 Computational Complexity
32(2)
2.6 Registering Images with a Known Rotational Difference
34(2)
2.7 Discussion
36(1)
2.8 Further Reading
37(3)
References
40(3)
3 Feature Point Detection 43(32)
3.1 Introduction
43(1)
3.2 Variant Features
44(6)
3.2.1 Central Moments
44(4)
3.2.2 Uniqueness
48(2)
3.3 Invariant Features
50(14)
3.3.1 Rotation-Invariant Features
50(8)
3.3.1.1 Laplacian of Gaussian (LoG) Detector
51(2)
3.3.1.2 Entropy
53(2)
3.3.1.3 Invariant Moments
55(3)
3.3.2 SIFT: A Scale-and Rotation-Invariant Point Detector
58(2)
3.3.3 Radiometric-Invariant Features
60(46)
3.3.3.1 Harris Corner Detector
60(3)
3.3.3.2 Hessian Corner Detector
63(1)
3.4 Performance Evaluation
64(4)
3.5 Further Reading
68(1)
References
68(7)
4 Feature Line Detection 75(58)
4.1 Hough Transform Using Polar Equation of Lines
79(3)
4.2 Hough Transform Using Slope and y-Intercept Equation of Lines
82(4)
4.3 Line Detection Using Parametric Equation of Lines
86(3)
4.4 Line Detection by Clustering
89(3)
4.5 Line Detection by Contour Tracing
92(3)
4.6 Line Detection by Curve Fitting
95(6)
4.7 Line Detection by Region Subdivision
101(5)
4.8 Comparison of the Line Detection Algorithms
106(11)
4.8.1 Sensitivity to Noise
106(1)
4.8.2 Positional and Directional Errors
106(3)
4.8.3 Length Accuracy
109(1)
4.8.4 Speed
109(1)
4.8.5 Quality of Detected Lines
109(8)
4.9 Revisiting Image Dominant Orientation Detection
117(4)
4.10 Further Reading
121(4)
References
125(8)
5 Finding Homologous Points 133(82)
5.1 Introduction
133(1)
5.2 Point Pattern Matching
134(12)
5.2.1 Parameter Estimation by Clustering
137(4)
5.2.2 Parameter Estimation by RANSAC
141(5)
5.3 Point Descriptors
146(14)
5.3.1 Histogram-Based Descriptors
147(1)
5.3.2 SIFT Descriptor
148(3)
5.3.3 GLOH Descriptor
151(1)
5.3.4 Composite Descriptors
152(8)
5.3.4.1 Hu Invariant Moments
152(1)
5.3.4.2 Complex Moments
152(1)
5.3.4.3 Cornerness Measures
153(1)
5.3.4.4 Power Spectrum Features
154(1)
5.3.4.5 Differential Features
155(1)
5.3.4.6 Spatial Domain Features
155(5)
5.4 Similarity Measures
160(7)
5.4.1 Correlation Coefficient
160(1)
5.4.2 Minimum Ratio
161(1)
5.4.3 Spearman's p
161(1)
5.4.4 Ordinal Measure
162(1)
5.4.5 Correlation Ratio
162(2)
5.4.6 Shannon Mutual Information
164(1)
5.4.7 Tsallis Mutual Information
165(1)
5.4.8 F-Information
166(1)
5.5 Distance Measures
167(3)
5.5.1 Sum of Absolute Differences
167(1)
5.5.2 Median of Absolute Differences
167(1)
5.5.3 Square Euclidean Distance
168(1)
5.5.4 Intensity-Ratio Variance
168(1)
5.5.5 Rank Distance
169(1)
5.5.6 Shannon Joint Entropy
169(1)
5.5.7 Exclusive F-Information
170(1)
5.6 Template Matching
170(8)
5.6.1 Coarse-to-Fine Matching
171(1)
5.6.2 Multistage Matching
172(1)
5.6.3 Rotationally Invariant Matching
173(1)
5.6.4 Gaussian-Weighted Template Matching
174(1)
5.6.5 Template Matching in Different Modality Rotated Images
175(3)
5.7 Robust Parameter Estimation
178(15)
5.7.1 Ordinary Least-Squares Estimator
180(2)
5.7.2 Weighted Least-Squares Estimator
182(2)
5.7.3 Least Median of Squares Estimator
184(1)
5.7.4 Least Trimmed Squares Estimator
184(1)
5.7.5 Rank Estimator
185(8)
5.8 Finding Optimal Transformation Parameters
193(1)
5.9 Performance Evaluation
193(4)
5.10 Further Reading
197(3)
References
200(15)
6 Finding Homologous Lines 215(46)
6.1 Introduction
215(1)
6.2 Determining Transformation Parameters from Line Parameters
215(6)
6.3 Finding Homologous Lines by Clustering
221(8)
6.3.1 Finding the Rotation Parameter
222(1)
6.3.2 Finding the Translation Parameters
223(6)
6.4 Finding Homologous Lines by RANSAC
229(3)
6.5 Line Grouping Using Local Image Information
232(3)
6.6 Line Grouping Using Vanishing Points
235(18)
6.6.1 Methods Searching the Image Space
235(1)
6.6.2 Methods Searching the Polar Space
236(1)
6.6.3 Methods Searching the Gaussian Sphere
236(1)
6.6.4 A Method Searching Both Image and Gaussian Sphere
237(7)
6.6.5 Measuring the Accuracy of Detected Vanishing Points
244(3)
6.6.6 Discussion
247(6)
6.7 Robust Parameter Estimation Using Homologous Lines
253(2)
6.8 Revisiting Image Dominant Orientation Detection
255(1)
6.9 Further Reading
256(1)
References
257(4)
7 Nonrigid Image Registration 261(38)
7.1 Introduction
261(1)
7.2 Finding Homologous Points
262(12)
7.2.1 Coarse-to-Fine Matching
262(7)
7.2.2 Correspondence by Template Matching
269(5)
7.3 Outlier Removal
274(4)
7.4 Elastic Transformation Models
278(14)
7.4.1 Surface Spline (SS) Interpolation
280(2)
7.4.2 Piecewise Linear (PWL) Interpolation
282(1)
7.4.3 Moving Least Squares (MLS) Approximation
283(2)
7.4.4 Weighted Linear (WL) Approximation
285(2)
7.4.5 Performance Evaluation
287(4)
7.4.6 Choosing the Right Transformation Model
291(1)
7.5 Further Reading
292(1)
References
293(6)
8 Volume Image Registration 299(44)
8.1 Introduction
299(2)
8.2 Feature Point Detection
301(6)
8.2.1 Central Moments
301(1)
8.2.2 Entropy
302(1)
8.2.3 LoG Operator
302(1)
8.2.4 First-Derivative Intensities
303(1)
8.2.5 Second-Derivative Intensities
304(1)
8.2.6 Speed-Up Considerations in Feature Point Detection
305(1)
8.2.7 Evaluation of Feature Point Detectors
305(2)
8.3 Finding Homologous Points
307(14)
8.3.1 Finding Initial Homologous Points Using Image Descriptors
310(3)
8.3.2 Finding Initial Homologous Points by Template Matching
313(2)
8.3.3 Finding Final Homologous Points from Coarse to Fine
315(5)
8.3.4 Finding the Final Homologous Points by Outlier Removal
320(1)
8.4 Transformation Models for Volume Image Registration
321(9)
8.4.1 Volume Spline
323(2)
8.4.2 Weighted Rigid Transformation
325(2)
8.4.3 Computing the Overall Transformation
327(3)
8.5 Performance Evaluation
330(5)
8.5.1 Accuracy
330(3)
8.5.2 Reliability
333(1)
8.5.3 Speed
333(2)
8.6 Further Reading
335(2)
References
337(6)
9 Validation Methods 343(14)
9.1 Introduction
343(1)
9.2 Validation Using Simulation Data
344(1)
9.3 Validation Using a Gold Standard
345(2)
9.4 Validation by an Expert Observer
347(1)
9.5 Validation Using a Consistency Measure
348(2)
9.6 Validation Using a Similarity/Distance Measure
350(1)
9.7 Further Reading
351(1)
References
352(5)
10 Video Image Registration 357(40)
Edgardo Molina
Wai Lun Khoo
Hao Tang
Zhigang Zhu
10.1 Introduction
357(1)
10.2 Motion Modeling
358(7)
10.2.1 The Motion Field of Rigid Objects
358(2)
10.2.2 Motion Models
360(5)
10.2.2.1 Pure Rotation and a 3-D Scene
361(1)
10.2.2.2 General Motion and a Planar Scene
362(1)
10.2.2.3 Translational Motion and a 3-D Scene
363(2)
10.3 Image Alignment
365(5)
10.3.1 Feature-Based Methods
367(2)
10.3.2 Mechanical-Based Methods
369(1)
10.4 Image Composition
370(4)
10.4.1 Compositing Surface
370(1)
10.4.2 Image Warping
371(2)
10.4.3 Pixel Selection and Blending
373(1)
10.5 Application Examples
374(19)
10.5.1 Pushbroom Stereo Mosaics Under Translational Motion
374(4)
10.5.1.1 Parallel-Perspective Geometry and Panoramas
374(2)
10.5.1.2 Stereo and Multiview Panoramas
376(2)
10.5.1.3 Results
378(1)
10.5.2 Stereo Mosaics when Moving a Camera on a Circular Path
378(4)
10.5.2.1 Circular Geometry
379(1)
10.5.2.2 Stereo Geometry
379(2)
10.5.2.3 Geometry and Results When Using PRISM
381(1)
10.5.3 Multimodal Panoramic Registration of Video Images
382(5)
10.5.3.1 Concentric Geometry
383(2)
10.5.3.2 Multimodal Alignment
385(2)
10.5.3.3 Results
387(1)
10.5.4 Video Mosaics Under General Motion
387(6)
10.5.4.1 Direct Layering Approach
389(3)
10.5.4.2 Multiple Runs and Results
392(1)
10.6 Further Reading
393(2)
References
395(2)
11 Multitemporal Image Registration 397(22)
11.1 Introduction
397(1)
11.2 Finding Transformation Parameters from Line Parameters
398(1)
11.3 Finding an Initial Set of Homologous Lines
399(4)
11.4 Maximizing the Number of Homologous Lines
403(3)
11.5 Examples of Multitemporal Image Registration
406(7)
11.6 Further Reading
413(2)
References
415(4)
12 Open Problems and Research Topics 419(14)
12.1 Finding Rotational Difference between Multimodality Images
419(1)
12.2 Designing a Robust Image Descriptor
420(1)
12.3 Finding Homologous Lines for Nonrigid Registration
421(2)
12.4 Nonrigid Registration Using Homologous Lines
423(1)
12.5 Transformation Models with Nonsymmetric Basis Functions
423(3)
12.6 Finding Homologous Points along Homologous Contours
426(3)
12.7 4-D Image Registration
429(1)
References
430(3)
Glossary 433(4)
Acronyms 437(2)
Symbols 439(2)
A Image Registration Software 441(46)
A.1
Chapter 2: Image Orientation Detection
441(3)
A.1.1 Introduction
441(1)
A.1.2 Operations
442(2)
A.2
Chapter 3: Feature Point Detection
444(4)
A.2.1 Introduction
444(1)
A.2.2 Operations
445(3)
A.3
Chapter 4: Feature Line Detection
448(4)
A.3.1 Introduction
448(1)
A.3.2 Operations
449(3)
A.4
Chapter 5: Finding Homologous Points
452(7)
A.4.1 Introduction
452(1)
A.4.2 Operations
452(7)
A.5
Chapter 6: Finding Homologous Lines
459(10)
A.5.1 Introduction
459(1)
A.5.2 Operations
460(9)
A.6
Chapter 7: Nonrigid Image Registration
469(10)
A.6.1 Introduction
469(1)
A.6.2 Operations
469(10)
A.7
Chapter 8: Volume Image Registration
479(8)
A.7.1 Introduction
479(1)
A.7.2 I/O File Formats
479(1)
A.7.3 Operations
480(7)
References 487(2)
Index 489
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).
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