Theory and Applications of Image Registration

 
 
Standards Information Network (Verlag)
  • erschienen am 5. Juli 2017
  • |
  • 520 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-17172-0 (ISBN)
 
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.
weitere Ausgaben werden ermittelt
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).
1 - Cover [Seite 1]
2 - Title Page [Seite 5]
3 - Copyright [Seite 6]
4 - Dedication [Seite 7]
5 - Contents [Seite 10]
6 - Contributors [Seite 17]
7 - Acknowledgments [Seite 19]
8 - About the Companion Website [Seite 21]
9 - Chapter 1 Introduction [Seite 23]
9.1 - 1.1 Organization of the Book [Seite 25]
9.2 - 1.2 Further Reading [Seite 27]
9.3 - References [Seite 27]
10 - Chapter 2 Image Orientation Detection [Seite 31]
10.1 - 2.1 Introduction [Seite 31]
10.2 - 2.2 Geometric Gradient and Geometric Smoothing [Seite 35]
10.2.1 - 2.2.1 Calculating Geometric Gradients [Seite 37]
10.3 - 2.3 Comparison of Geometric Gradients and Intensity Gradients [Seite 40]
10.4 - 2.4 Finding the Rotational Difference between Two Images [Seite 43]
10.5 - 2.5 Performance Evaluation [Seite 45]
10.5.1 - 2.5.1 Reliability [Seite 45]
10.5.2 - 2.5.2 Accuracy [Seite 53]
10.5.3 - 2.5.3 Computational Complexity [Seite 54]
10.6 - 2.6 Registering Images with a Known Rotational Difference [Seite 56]
10.7 - 2.7 Discussion [Seite 58]
10.8 - 2.8 Further Reading [Seite 59]
10.9 - References [Seite 62]
11 - Chapter 3 Feature Point Detection [Seite 65]
11.1 - 3.1 Introduction [Seite 65]
11.2 - 3.2 Variant Features [Seite 66]
11.2.1 - 3.2.1 Central Moments [Seite 66]
11.2.2 - 3.2.2 Uniqueness [Seite 70]
11.3 - 3.3 Invariant Features [Seite 72]
11.3.1 - 3.3.1 Rotation-Invariant Features [Seite 72]
11.3.1.1 - 3.3.1.1 Laplacian of Gaussian (LoG) Detector [Seite 73]
11.3.1.2 - 3.3.1.2 Entropy [Seite 75]
11.3.1.3 - 3.3.1.3 Invariant Moments [Seite 77]
11.3.2 - 3.3.2 SIFT: A Scale-and Rotation-Invariant Point Detector [Seite 80]
11.3.3 - 3.3.3 Radiometric-Invariant Features [Seite 82]
11.3.3.1 - 3.3.3.1 Harris Corner Detector [Seite 82]
11.3.3.2 - 3.3.3.2 Hessian Corner Detector [Seite 85]
11.4 - 3.4 Performance Evaluation [Seite 86]
11.5 - 3.5 Further Reading [Seite 90]
11.6 - References [Seite 90]
12 - Chapter 4 Feature Line Detection [Seite 97]
12.1 - 4.1 Hough Transform Using Polar Equation of Lines [Seite 101]
12.2 - 4.2 Hough Transform Using Slope and y-Intercept Equation of Lines [Seite 104]
12.3 - 4.3 Line Detection Using Parametric Equation of Lines [Seite 108]
12.4 - 4.4 Line Detection by Clustering [Seite 111]
12.5 - 4.5 Line Detection by Contour Tracing [Seite 114]
12.6 - 4.6 Line Detection by Curve Fitting [Seite 117]
12.7 - 4.7 Line Detection by Region Subdivision [Seite 123]
12.8 - 4.8 Comparison of the Line Detection Algorithms [Seite 128]
12.8.1 - 4.8.1 Sensitivity to Noise [Seite 128]
12.8.2 - 4.8.2 Positional and Directional Errors [Seite 128]
12.8.3 - 4.8.3 Length Accuracy [Seite 131]
12.8.4 - 4.8.4 Speed [Seite 131]
12.8.5 - 4.8.5 Quality of Detected Lines [Seite 131]
12.9 - 4.9 Revisiting Image Dominant Orientation Detection [Seite 139]
12.10 - 4.10 Further Reading [Seite 143]
12.11 - References [Seite 147]
13 - Chapter 5 Finding Homologous Points [Seite 155]
13.1 - 5.1 Introduction [Seite 155]
13.2 - 5.2 Point Pattern Matching [Seite 156]
13.2.1 - 5.2.1 Parameter Estimation by Clustering [Seite 159]
13.2.2 - 5.2.2 Parameter Estimation by RANSAC [Seite 163]
13.3 - 5.3 Point Descriptors [Seite 168]
13.3.1 - 5.3.1 Histogram-Based Descriptors [Seite 169]
13.3.2 - 5.3.2 SIFT Descriptor [Seite 170]
13.3.3 - 5.3.3 GLOH Descriptor [Seite 173]
13.3.4 - 5.3.4 Composite Descriptors [Seite 174]
13.3.4.1 - 5.3.4.1 Hu Invariant Moments [Seite 174]
13.3.4.2 - 5.3.4.2 Complex Moments [Seite 174]
13.3.4.3 - 5.3.4.3 Cornerness Measures [Seite 175]
13.3.4.4 - 5.3.4.4 Power Spectrum Features [Seite 176]
13.3.4.5 - 5.3.4.5 Differential Features [Seite 177]
13.3.4.6 - 5.3.4.6 Spatial Domain Features [Seite 177]
13.4 - 5.4 Similarity Measures [Seite 182]
13.4.1 - 5.4.1 Correlation Coefficient [Seite 182]
13.4.2 - 5.4.2 Minimum Ratio [Seite 183]
13.4.3 - 5.4.3 Spearman's ?????? [Seite 183]
13.4.4 - 5.4.4 Ordinal Measure [Seite 184]
13.4.5 - 5.4.5 Correlation Ratio [Seite 184]
13.4.6 - 5.4.6 Shannon Mutual Information [Seite 186]
13.4.7 - 5.4.7 Tsallis Mutual Information [Seite 187]
13.4.8 - 5.4.8 F-Information [Seite 188]
13.5 - 5.5 Distance Measures [Seite 189]
13.5.1 - 5.5.1 Sum of Absolute Differences [Seite 189]
13.5.2 - 5.5.2 Median of Absolute Differences [Seite 189]
13.5.3 - 5.5.3 Square Euclidean Distance [Seite 190]
13.5.4 - 5.5.4 Intensity-Ratio Variance [Seite 190]
13.5.5 - 5.5.5 Rank Distance [Seite 191]
13.5.6 - 5.5.6 Shannon Joint Entropy [Seite 191]
13.5.7 - 5.5.7 Exclusive F-Information [Seite 192]
13.6 - 5.6 Template Matching [Seite 192]
13.6.1 - 5.6.1 Coarse-to-Fine Matching [Seite 193]
13.6.2 - 5.6.2 Multistage Matching [Seite 194]
13.6.3 - 5.6.3 Rotationally Invariant Matching [Seite 195]
13.6.4 - 5.6.4 Gaussian-Weighted Template Matching [Seite 196]
13.6.5 - 5.6.5 Template Matching in Different Modality Rotated Images [Seite 197]
13.7 - 5.7 Robust Parameter Estimation [Seite 200]
13.7.1 - 5.7.1 Ordinary Least-Squares Estimator [Seite 202]
13.7.2 - 5.7.2 Weighted Least-Squares Estimator [Seite 204]
13.7.3 - 5.7.3 Least Median of Squares Estimator [Seite 206]
13.7.4 - 5.7.4 Least Trimmed Squares Estimator [Seite 206]
13.7.5 - 5.7.5 Rank Estimator [Seite 207]
13.8 - 5.8 Finding Optimal Transformation Parameters [Seite 215]
13.9 - 5.9 Performance Evaluation [Seite 215]
13.10 - 5.10 Further Reading [Seite 219]
13.11 - References [Seite 222]
14 - Chapter 6 Finding Homologous Lines [Seite 237]
14.1 - 6.1 Introduction [Seite 237]
14.2 - 6.2 Determining Transformation Parameters from Line Parameters [Seite 237]
14.3 - 6.3 Finding Homologous Lines by Clustering [Seite 243]
14.3.1 - 6.3.1 Finding the Rotation Parameter [Seite 244]
14.3.2 - 6.3.2 Finding the Translation Parameters [Seite 245]
14.4 - 6.4 Finding Homologous Lines by RANSAC [Seite 251]
14.5 - 6.5 Line Grouping Using Local Image Information [Seite 254]
14.6 - 6.6 Line Grouping Using Vanishing Points [Seite 257]
14.6.1 - 6.6.1 Methods Searching the Image Space [Seite 257]
14.6.2 - 6.6.2 Methods Searching the Polar Space [Seite 258]
14.6.3 - 6.6.3 Methods Searching the Gaussian Sphere [Seite 258]
14.6.4 - 6.6.4 A Method Searching Both Image and Gaussian Sphere [Seite 259]
14.6.5 - 6.6.5 Measuring the Accuracy of Detected Vanishing Points [Seite 266]
14.6.6 - 6.6.6 Discussion [Seite 269]
14.7 - 6.7 Robust Parameter Estimation Using Homologous Lines [Seite 275]
14.8 - 6.8 Revisiting Image Dominant Orientation Detection [Seite 277]
14.9 - 6.9 Further Reading [Seite 278]
14.10 - References [Seite 279]
15 - Chapter 7 Nonrigid Image Registration [Seite 283]
15.1 - 7.1 Introduction [Seite 283]
15.2 - 7.2 Finding Homologous Points [Seite 284]
15.2.1 - 7.2.1 Coarse-to-Fine Matching [Seite 284]
15.2.2 - 7.2.2 Correspondence by Template Matching [Seite 291]
15.3 - 7.3 Outlier Removal [Seite 296]
15.4 - 7.4 Elastic Transformation Models [Seite 300]
15.4.1 - 7.4.1 Surface Spline (SS) Interpolation [Seite 302]
15.4.2 - 7.4.2 Piecewise Linear (PWL) Interpolation [Seite 304]
15.4.3 - 7.4.3 Moving Least Squares (MLS) Approximation [Seite 305]
15.4.4 - 7.4.4 Weighted Linear (WL) Approximation [Seite 307]
15.4.5 - 7.4.5 Performance Evaluation [Seite 309]
15.4.6 - 7.4.6 Choosing the Right Transformation Model [Seite 313]
15.5 - 7.5 Further Reading [Seite 314]
15.6 - References [Seite 315]
16 - Chapter 8 Volume Image Registration [Seite 321]
16.1 - 8.1 Introduction [Seite 321]
16.2 - 8.2 Feature Point Detection [Seite 323]
16.2.1 - 8.2.1 Central Moments [Seite 323]
16.2.2 - 8.2.2 Entropy [Seite 324]
16.2.3 - 8.2.3 LoG Operator [Seite 324]
16.2.4 - 8.2.4 First-Derivative Intensities [Seite 325]
16.2.5 - 8.2.5 Second-Derivative Intensities [Seite 326]
16.2.6 - 8.2.6 Speed-Up Considerations in Feature Point Detection [Seite 327]
16.2.7 - 8.2.7 Evaluation of Feature Point Detectors [Seite 327]
16.3 - 8.3 Finding Homologous Points [Seite 329]
16.3.1 - 8.3.1 Finding Initial Homologous Points Using Image Descriptors [Seite 332]
16.3.2 - 8.3.2 Finding Initial Homologous Points by Template Matching [Seite 335]
16.3.3 - 8.3.3 Finding Final Homologous Points from Coarse to Fine [Seite 337]
16.3.4 - 8.3.4 Finding the Final Homologous Points by Outlier Removal [Seite 342]
16.4 - 8.4 Transformation Models for Volume Image Registration [Seite 343]
16.4.1 - 8.4.1 Volume Spline [Seite 345]
16.4.2 - 8.4.2 Weighted Rigid Transformation [Seite 347]
16.4.3 - 8.4.3 Computing the Overall Transformation [Seite 349]
16.5 - 8.5 Performance Evaluation [Seite 352]
16.5.1 - 8.5.1 Accuracy [Seite 352]
16.5.2 - 8.5.2 Reliability [Seite 355]
16.5.3 - 8.5.3 Speed [Seite 355]
16.6 - 8.6 Further Reading [Seite 357]
16.7 - References [Seite 359]
17 - Chapter 9 Validation Methods [Seite 365]
17.1 - 9.1 Introduction [Seite 365]
17.2 - 9.2 Validation Using Simulation Data [Seite 366]
17.3 - 9.3 Validation Using a Gold Standard [Seite 367]
17.4 - 9.4 Validation by an Expert Observer [Seite 369]
17.5 - 9.5 Validation Using a Consistency Measure [Seite 370]
17.6 - 9.6 Validation Using a Similarity/Distance Measure [Seite 372]
17.7 - 9.7 Further Reading [Seite 373]
17.8 - References [Seite 374]
18 - Chapter 10 Video Image Registration [Seite 379]
18.1 - 10.1 Introduction [Seite 379]
18.2 - 10.2 Motion Modeling [Seite 380]
18.2.1 - 10.2.1 The Motion Field of Rigid Objects [Seite 380]
18.2.2 - 10.2.2 Motion Models [Seite 382]
18.2.2.1 - 10.2.2.1 Pure Rotation and a 3-D Scene [Seite 383]
18.2.2.2 - 10.2.2.2 General Motion and a Planar Scene [Seite 384]
18.2.2.3 - 10.2.2.3 Translational Motion and a 3-D Scene [Seite 385]
18.3 - 10.3 Image Alignment [Seite 387]
18.3.1 - 10.3.1 Feature-Based Methods [Seite 389]
18.3.2 - 10.3.2 Mechanical-Based Methods [Seite 391]
18.4 - 10.4 Image Composition [Seite 392]
18.4.1 - 10.4.1 Compositing Surface [Seite 392]
18.4.2 - 10.4.2 Image Warping [Seite 393]
18.4.3 - 10.4.3 Pixel Selection and Blending [Seite 395]
18.5 - 10.5 Application Examples [Seite 396]
18.5.1 - 10.5.1 Pushbroom Stereo Mosaics Under Translational Motion [Seite 396]
18.5.1.1 - 10.5.1.1 Parallel-Perspective Geometry and Panoramas [Seite 396]
18.5.1.2 - 10.5.1.2 Stereo and Multiview Panoramas [Seite 398]
18.5.1.3 - 10.5.1.3 Results [Seite 400]
18.5.2 - 10.5.2 Stereo Mosaics when Moving a Camera on a Circular Path [Seite 400]
18.5.2.1 - 10.5.2.1 Circular Geometry [Seite 401]
18.5.2.2 - 10.5.2.2 Stereo Geometry [Seite 401]
18.5.2.3 - 10.5.2.3 Geometry and Results When Using PRISM [Seite 403]
18.5.3 - 10.5.3 Multimodal Panoramic Registration of Video Images [Seite 404]
18.5.3.1 - 10.5.3.1 Concentric Geometry [Seite 405]
18.5.3.2 - 10.5.3.2 Multimodal Alignment [Seite 407]
18.5.3.3 - 10.5.3.3 Results [Seite 409]
18.5.4 - 10.5.4 Video Mosaics Under General Motion [Seite 409]
18.5.4.1 - 10.5.4.1 Direct Layering Approach [Seite 411]
18.5.4.2 - 10.5.4.2 Multiple Runs and Results [Seite 414]
18.6 - 10.6 Further Reading [Seite 415]
18.7 - References [Seite 417]
19 - Chapter 11 Multitemporal Image Registration [Seite 419]
19.1 - 11.1 Introduction [Seite 419]
19.2 - 11.2 Finding Transformation Parameters from Line Parameters [Seite 420]
19.3 - 11.3 Finding an Initial Set of Homologous Lines [Seite 421]
19.4 - 11.4 Maximizing the Number of Homologous Lines [Seite 425]
19.5 - 11.5 Examples of Multitemporal Image Registration [Seite 428]
19.6 - 11.6 Further Reading [Seite 435]
19.7 - References [Seite 437]
20 - Chapter 12 Open Problems and Research Topics [Seite 441]
20.1 - 12.1 Finding Rotational Difference between Multimodality Images [Seite 441]
20.2 - 12.2 Designing a Robust Image Descriptor [Seite 442]
20.3 - 12.3 Finding Homologous Lines for Nonrigid Registration [Seite 443]
20.4 - 12.4 Nonrigid Registration Using Homologous Lines [Seite 445]
20.5 - 12.5 Transformation Models with Nonsymmetric Basis Functions [Seite 445]
20.6 - 12.6 Finding Homologous Points along Homologous Contours [Seite 448]
20.7 - 12.7 4-D Image Registration [Seite 451]
20.8 - References [Seite 452]
21 - Glossary [Seite 455]
22 - Acronyms [Seite 459]
23 - Symbols [Seite 461]
24 - Appendix A: Image Registration Software [Seite 463]
24.1 - A.1 Chapter 2: Image Orientation Detection [Seite 463]
24.1.1 - A.1.1 Introduction [Seite 463]
24.1.2 - A.1.2 Operations [Seite 464]
24.2 - A.2 Chapter 3: Feature Point Detection [Seite 466]
24.2.1 - A.2.1 Introduction [Seite 466]
24.2.2 - A.2.2 Operations [Seite 467]
24.3 - A.3 Chapter 4: Feature Line Detection [Seite 470]
24.3.1 - A.3.1 Introduction [Seite 470]
24.3.2 - A.3.2 Operations [Seite 471]
24.4 - A.4 Chapter 5: Finding Homologous Points [Seite 474]
24.4.1 - A.4.1 Introduction [Seite 474]
24.4.2 - A.4.2 Operations [Seite 474]
24.5 - A.5 Chapter 6: Finding Homologous Lines [Seite 481]
24.5.1 - A.5.1 Introduction [Seite 481]
24.5.2 - A.5.2 Operations [Seite 482]
24.6 - A.6 Chapter 7: Nonrigid Image Registration [Seite 491]
24.6.1 - A.6.1 Introduction [Seite 491]
24.6.2 - A.6.2 Operations [Seite 491]
24.7 - A.7 Chapter 8: Volume Image Registration [Seite 501]
24.7.1 - A.7.1 Introduction [Seite 501]
24.7.2 - A.7.2 I/O File Formats [Seite 501]
24.7.3 - A.7.3 Operations [Seite 502]
24.8 - References [Seite 509]
25 - Index [Seite 511]
26 - EULA [Seite 516]

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