2D and 3D Image Analysis by Moments (Hardcover)

<內容簡介>

Presents recent significant and rapid development in the field of 2D and 3D image analysis

2D and 3D Image Analysis by Moments, is a unique compendium of moment-based image analysis which includes traditional methods and also reflects the latest development of the field.

The book presents a survey of 2D and 3D moment invariants with respect to similarity and affine spatial transformations and to image blurring and smoothing by various filters. The book comprehensively describes the mathematical background and theorems about the invariants but a large part is also devoted to practical usage of moments. Applications from various fields of computer vision, remote sensing, medical imaging, image retrieval, watermarking, and forensic analysis are demonstrated. Attention is also paid to efficient algorithms of moment computation.

Key features:

*Presents a systematic overview of moment-based features used in 2D and 3D image analysis.
*Demonstrates invariant properties of moments with respect to various spatial and intensity transformations.
*Reviews and compares several orthogonal polynomials and respective moments.
*Describes efficient numerical algorithms for moment computation.
*It is a "classroom ready" textbook with a self-contained introduction to classifier design.
*The accompanying website contains around 300 lecture slides, Matlab codes, complete lists of the invariants, test images, and other supplementary material.

2D and 3D Image Analysis by Moments, is ideal for mathematicians, computer scientists, engineers, software developers, and Ph.D students involved in image analysis and recognition. Due to the addition of two introductory chapters on classifier design, the book may also serve as a self-contained textbook for graduate university courses on object recognition.

<章節目錄>

Preface xvii

Acknowledgements xxi

1 Motivation 1

1.1 Image analysis by computers 1

1.2 Humans, computers, and object recognition 4

1.3 Outline of the book 5

References 7

2 Introduction to Object Recognition 8

2.1 Feature space 8

2.1.1 Metric spaces and norms 9

2.1.2 Equivalence and partition 11

2.1.3 Invariants 12

2.1.4 Covariants 14

2.1.5 Invariant-less approaches 15

2.2 Categories of the invariants 15

2.2.1 Simple shape features 16

2.2.2 Complete visual features 18

2.2.3 Transformation coefficient features 20

2.2.4 Textural features 21

2.2.5 Wavelet-based features 23

2.2.6 Differential invariants 24

2.2.7 Point set invariants 25

2.2.8 Moment invariants 26

2.3 Classifiers 27

2.3.1 Nearest-neighbor classifiers 28

2.3.2 Support vector machines 31

2.3.3 Neural network classifiers 32

2.3.4 Bayesian classifier 34

2.3.5 Decision trees 35

2.3.6 Unsupervised classification 36

2.4 Performance of the classifiers 37

2.4.1 Measuring the classifier performance 37

2.4.2 Fusing classifiers 38

2.4.3 Reduction of the feature space dimensionality 38

2.5 Conclusion 40

References 41

3 2D Moment Invariants to Translation, Rotation, and Scaling 45

3.1 Introduction 45

3.1.1 Mathematical preliminaries 45

3.1.2 Moments 47

3.1.3 Geometric moments in 2D 48

3.1.4 Other moments 49

3.2 TRS invariants from geometric moments 50

3.2.1 Invariants to translation 50

3.2.2 Invariants to uniform scaling 51

3.2.3 Invariants to non-uniform scaling 52

3.2.4 Traditional invariants to rotation 54

3.3 Rotation invariants using circular moments 56

3.4 Rotation invariants from complex moments 57

3.4.1 Complex moments 57

3.4.2 Construction of rotation invariants 58

3.4.3 Construction of the basis 59

3.4.4 Basis of the invariants of the second and third orders 62

3.4.5 Relationship to the Hu invariants 63

3.5 Pseudoinvariants 67

3.6 Combined invariants to TRS and contrast stretching 68

3.7 Rotation invariants for recognition of symmetric objects 69

3.7.1 Logo recognition 75

3.7.2 Recognition of shapes with different fold numbers 75

3.7.3 Experiment with a baby toy 77

3.8 Rotation invariants via image normalization 81

3.9 Moment invariants of vector fields 86

3.10 Conclusion 92

References 92

4 3D Moment Invariants to Translation, Rotation, and Scaling 95

4.1 Introduction 95

4.2 Mathematical description of the 3D rotation 98

4.3 Translation and scaling invariance of 3D geometric moments 100

4.4 3D rotation invariants by means of tensors 101

4.4.1 Tensors 101

4.4.2 Rotation invariants 102

4.4.3 Graph representation of the invariants 103

4.4.4 The number of the independent invariants 104

4.4.5 Possible dependencies among the invariants 105

4.4.6 Automatic generation of the invariants by the tensor method 106

4.5 Rotation invariants from 3D complex moments 108

4.5.1 Translation and scaling invariance of 3D complex moments 112

4.5.2 Invariants to rotation by means of the group representation theory 112

4.5.3 Construction of the rotation invariants 115

4.5.4 Automated generation of the invariants 117

4.5.5 Elimination of the reducible invariants 118

4.5.6 The irreducible invariants 118

4.6 3D translation, rotation, and scale invariants via normalization 119

4.6.1 Rotation normalization by geometric moments 120

4.6.2 Rotation normalization by complex moments 123

4.7 Invariants of symmetric objects 124

4.7.1 Rotation and reflection symmetry in 3D 124

4.7.2 The influence of symmetry on 3D complex moments 128

4.7.3 Dependencies among the invariants due to symmetry 130

4.8 Invariants of 3D vector fields 131

4.9 Numerical experiments 131

4.9.1 Implementation details 131

4.9.2 Experiment with archeological findings 133

4.9.3 Recognition of generic classes 135

4.9.4 Submarine recognition – robustness to noise test 137

4.9.5 Teddy bears – the experiment on real data 141

4.9.6 Artificial symmetric bodies 142

4.9.7 Symmetric objects from the Princeton Shape Benchmark 143

4.10 Conclusion 147

Appendix 4.A 148

Appendix 4.B 156

Appendix 4.C 158

References 160

5 Affine Moment Invariants in 2D and 3D 163

5.1 Introduction 163

5.1.1 2D projective imaging of 3D world 164

5.1.2 Projective moment invariants 165

5.1.3 Affine transformation 167

5.1.4 2D Affine moment invariants – the history 168

5.2 AMIs derived from the Fundamental theorem 170

5.3 AMIs generated by graphs 171

5.3.1 The basic concept 172

5.3.2 Representing the AMIs by graphs 173

5.3.3 Automatic generation of the invariants by the graph method 173

5.3.4 Independence of the AMIs 174

5.3.5 The AMIs and tensors 180

5.4 AMIs via image normalization 181

5.4.1 Decomposition of the affine transformation 182

5.4.2 Relation between the normalized moments and the AMIs 185

5.4.3 Violation of stability 186

5.4.4 Affine invariants via half normalization 187

5.4.5 Affine invariants from complex moments 187

5.5 The method of the transvectants 190

5.6 Derivation of the AMIs from the Cayley-Aronhold equation 195

5.6.1 Manual solution 195

5.6.2 Automatic solution 198

5.7 Numerical experiments 201

5.7.1 Invariance and robustness of the AMIs 201

5.7.2 Digit recognition 201

5.7.3 Recognition of symmetric patterns 204

5.7.4 The children’s mosaic 208

5.7.5 Scrabble tiles recognition 210

5.8 Affine invariants of color images 214

5.8.1 Recognition of color pictures 217

5.9 Affine invariants of 2D vector fields 218

5.10 3D affine moment invariants 221

5.10.1 The method of geometric primitives 222

5.10.2 Normalized moments in 3D 224

5.10.3 Cayley-Aronhold equation in 3D 225

5.11 Beyond invariants 225

5.11.1 Invariant distance measure between images 225

5.11.2 Moment matching 227

5.11.3 Object recognition as a minimization problem 229

5.11.4 Numerical experiments 229

5.12 Conclusion 231

Appendix 5.A 232

Appendix 5.B 233

References 234

6 Invariants to Image Blurring 237

6.1 Introduction 237

6.1.1 Image blurring – the sources and modeling 237

6.1.2 The need for blur invariants 239

6.1.3 State of the art of blur invariants 239

6.1.4 The chapter outline 246

6.2 An intuitive approach to blur invariants 247

6.3 Projection operators and blur invariants in Fourier domain 249

6.4 Blur invariants from image moments 252

6.5 Invariants to centrosymmetric blur 254

6.6 Invariants to circular blur 256

6.7 Invariants to N-FRS blur 259

6.8 Invariants to dihedral blur 265

6.9 Invariants to directional blur 269

6.10 Invariants to Gaussian blur 272

6.10.1 1D Gaussian blur invariants 274

6.10.2 Multidimensional Gaussian blur invariants 278

6.10.3 2D Gaussian blur invariants from complex moments 279

6.11 Invariants to other blurs 280

6.12 Combined invariants to blur and spatial transformations 282

6.12.1 Invariants to blur and rotation 282

6.12.2 Invariants to blur and affine transformation 283

6.13 Computational issues 284

6.14 Experiments with blur invariants 285

6.14.1 A simple test of blur invariance property 285

6.14.2 Template matching in satellite images 286

6.14.3 Template matching in outdoor images 291

6.14.4 Template matching in astronomical images 291

6.14.5 Face recognition on blurred and noisy photographs 292

6.14.6 Traffic sign recognition 294

6.15 Conclusion 302

Appendix 6.A 303

Appendix 6.B 304

Appendix 6.C 306

Appendix 6.D 308

Appendix 6.E 310

Appendix 6.F 310

Appendix 6.G 311

References 315

7 2D and 3D Orthogonal Moments 320

7.1 Introduction 320

7.2 2D moments orthogonal on a square 322

7.2.1 Hypergeometric functions 323

7.2.2 Legendre moments 324

7.2.3 Chebyshev moments 327

7.2.4 Gaussian-Hermite moments 331

7.2.5 Other moments orthogonal on a square 334

7.2.6 Orthogonal moments of a discrete variable 338

7.2.7 Rotation invariants from moments orthogonal on a square 348

7.3 2D moments orthogonal on a disk 351

7.3.1 Zernike and Pseudo-Zernike moments 352

7.3.2 Fourier-Mellin moments 358

7.3.3 Other moments orthogonal on a disk 361

7.4 Object recognition by Zernike moments 363

7.5 Image reconstruction from moments 365

7.5.1 Reconstruction by direct calculation 367

7.5.2 Reconstruction in the Fourier domain 369

7.5.3 Reconstruction from orthogonal moments 370

7.5.4 Reconstruction from noisy data 373

7.5.5 Numerical experiments with a reconstruction from OG moments 373

7.6 3D orthogonal moments 377

7.6.1 3D moments orthogonal on a cube 380

7.6.2 3D moments orthogonal on a sphere 381

7.6.3 3D moments orthogonal on a cylinder 383

7.6.4 Object recognition of 3D objects by orthogonal moments 383

7.6.5 Object reconstruction from 3D moments 387

7.7 Conclusion 389

References 389

8 Algorithms for Moment Computation 398

8.1 Introduction 398

8.2 Digital image and its moments 399

8.2.1 Digital image 399

8.2.2 Discrete moments 400

8.3 Moments of binary images 402

8.3.1 Moments of a rectangle 402

8.3.2 Moments of a general-shaped binary object 403

8.4 Boundary-based methods for binary images 404

8.4.1 The methods based on Green’s theorem 404

8.4.2 The methods based on boundary approximations 406

8.4.3 Boundary-based methods for 3D objects 407

8.5 Decomposition methods for binary images 410

8.5.1 The "delta" method 412

8.5.2 Quadtree decomposition 413

8.5.3 Morphological decomposition 415

8.5.4 Graph-based decomposition 416

8.5.5 Computing binary OG moments by means of decomposition methods 420

8.5.6 Experimental comparison of decomposition methods 422

8.5.7 3D decomposition methods 423

8.6 Geometric moments of graylevel images 428

8.6.1 Intensity slicing 429

8.6.2 Bit slicing 430

8.6.3 Approximation methods 433

8.7 Orthogonal moments of graylevel images 435

8.7.1 Recurrent relations for moments orthogonal on a square 435

8.7.2 Recurrent relations for moments orthogonal on a disk 436

8.7.3 Other methods 438

8.8 Conclusion 440

Appendix 8.A 441

References 443

9 Applications 448

9.1 Introduction 448

9.2 Image understanding 448

9.2.1 Recognition of animals 449

9.2.2 Face and other human parts recognition 450

9.2.3 Character and logo recognition 453

9.2.4 Recognition of vegetation and of microscopic natural structures 454

9.2.5 Traffic-related recognition 455

9.2.6 Industrial recognition 456

9.2.7 Miscellaneous applications 457

9.3 Image registration 459

9.3.1 Landmark-based registration 460

9.3.2 Landmark-free registration methods 467

9.4 Robot and autonomous vehicle navigation and visual servoing 470

9.5 Focus and image quality measure 474

9.6 Image retrieval 476

9.7 Watermarking 481

9.8 Medical imaging 486

9.9 Forensic applications 489

9.10 Miscellaneous applications 496

9.10.1 Noise resistant optical flow estimation 496

9.10.2 Edge detection 497

9.10.3 Description of solar flares 498

9.10.4 Gas-liquid flow categorization 499

9.10.5 3D object visualization 500

9.10.6 Object tracking 500

9.11 Conclusion 501

References 501

10 Conclusion 518

10.1 Summary of the book 518

10.2 Pros and cons of moment invariants 519

10.3 Outlook to the future 520

Index 521

內容簡介：

《2D和3D影像分析：基於矩的方法》介紹了近期在2D和3D影像分析領域中的重要和快速發展。這本書是一本獨特的基於矩的影像分析彙編，包括傳統方法，同時反映了該領域的最新發展。

該書介紹了關於相似性和仿射空間變換以及圖像模糊和平滑的2D和3D矩不變量的調查。該書全面描述了矩不變量的數學背景和定理，但也有很大一部分專門介紹了矩的實際應用。書中展示了來自計算機視覺、遙感、醫學影像、圖像檢索、數字水印和法醫分析等各個領域的應用。同時，該書還關注矩計算的高效算法。

主要特點：

- 系統地概述了在2D和3D影像分析中使用的基於矩的特徵。
- 示範了矩對於各種空間和強度變換的不變性特性。
- 回顧並比較了幾種正交多項式和相應的矩。
- 描述了高效的矩計算數值算法。
- 是一本“課堂準備就緒”的教科書，包含了自成一體的分類器設計介紹。
- 附帶的網站包含約300張講義幻燈片、Matlab代碼、完整的不變量列表、測試圖像和其他補充材料。

《2D和3D影像分析：基於矩的方法》適合數學家、計算機科學家、工程師、軟件開發人員和從事影像分析和識別的博士生。由於增加了兩個關於分類器設計的引言章節，該書也可以作為獨立的研究生課程教材，用於物體識別的大學課程。

章節目錄：

前言 xvii
致謝 xxi
1 動機 1
1.1 電腦影像分析 1
1.2 人類、電腦和物體識別 4
1.3 本書概述 5
參考文獻 7
2 物體識別簡介 8
2.1 特徵空間 8
2.1.1 度量空間和範數 9
2.1.2 等價和分割 11
2.1.3 不變量 12
2.1.4 協變量 14
2.1.5 無不變量方法 15
2.2 不變量的類別 15
2.2.1 簡單形狀特徵 16
2.2.2 完整的視覺特徵 18
2.2.3 變換係數特徵 20
2.2.4 紋理特徵 21
2.2.5 小波基特徵 23
2.2.6 微分不變量 24
2.2.7 點集不變量 25
2.2.8 矩不變量 26