상세정보

This introduction to the field of computer vision focuses on basic concepts and techniques. The thrust is to give practitioners what they need to know to develop a practical machine vision system. Binary vision, segmentation, constraint propagation techniques are presented as are camera calibration, color and texture, detection of motion, and object recognition. This text is appropriate for use in Computer Science and Electrical Engineering departments at the senior and graduate level.

정보제공 :

CONTENTS
Preface = xvii
Acknowledgments = xix
1 Introduction = 1
 1.1 Machine Vision = 1
 1.2 Relationships to Other Fields = 4
 1.3 Role of Knowledge = 5
 1.4 Image Geometry = 6
  1.4.1 Perspective Projection = 8
  1.4.2 Coordinate Systems = 9
 1.5 Sampling and Quantization = 10
 1.6 Image Definitions = 12
 1.7 Levels of Computation = 13
  1.7.1 Point Level = 14
  1.7.2 Local Level = 15
  1.7.3 Global Level = 16
  1.7.4 Object Level = 17
 1.8 Road Map = 18
2 Binary Image Processing = 25
 2.1 Thresholding = 28
 2.2 Geometric Properties = 31
  2.2.1 Size = 31
  2.2.2 Position = 32
  2.2.3 Orientation = 33
 2.3 Projections = 35
 2.4 Run-Length Encoding = 38
 2.5 Binary Algorithms = 39
  2.5.1 Definitions = 40
  2.5.2 Component Labeling = 44
  2.5.3 Size Filter = 47
  2.5.4 Euler Number = 48
  2.5.5 Region Boundary = 50
  2.5.6 Area and Perimeter = 50
  2.5.7 Compactness = 51
  2.5.8 Distance Measures = 52
  2.5.9 Distance Transforms = 53
  2.5.10 Medial Axis = 55
  2.5.11 Thinning = 57
  2.5.12 Expanding and Shrinking = 60
 2.6 Morphological Operators = 61
 2.7 Optical Character Recognition = 70
3 Regions = 73
 3.1 Regions and Edges = 73
 3.2 Region Segmentation = 76
  3.2.1 Automatic Thresholding = 76
  3.2.2 Limitations of Histogram Methods = 86
 3.3 Region Representation = 86
  3.3.1 Array Representation = 88
  3.3.2 Hierarchical Representations = 88
  3.3.3 Symbolic Representations = 90
  3.3.4 Data Structures for Segmentation = 92
 3.4 Split and Merge = 96
  3.4.1 Region Merging = 97
  3.4.2 Removing Weak Edges = 100
  3.4.3 Region Splitting = 103
  3.4.4 Split and Merge = 104
 3.5 Region Growing = 105
4 Image Filtering = 112
 4.1 Histogram Modification = 112
 4.2 Linear Systems = 115
 4.3 Linear Filters = 118
 4.4 Median Filter = 122
 4.5 Gaussian Smoothing = 123
  4.5.1 Rotational Symmetry = 127
  4.5.2 Fourier Transform Property = 128
  4.5.3 Gaussian Separability = 129
  4.5.4 Cascading Gaussians = 129
  4.5.5 Designing Gaussian Filters = 132
  4.5.6 Discrete Gaussian Filters = 134
5 Edge Detection = 140
 5.1 Gradient = 143
 5.2 Steps in Edge Detection = 145
  5.2.1 Roberts Operator = 146
  5.2.2 Sobel Operator = 147
  5.2.3 Prewitt Operator = 148
  5.2.4 Comparison = 148
 5.3 Second Derivative Operators = 149
  5.3.1 Laplacian Operator = 149
  5.3.2 Second Directional Derivative = 156
 5.4 Laplacian of Gaussian = 157
 5.5 Image Approximation = 162
 5.6 Gaussian Edge Detection = 168
  5.6.1 Canny Edge Detection = 169
 5.7 Subpixel Location Estimation = 173
 5.8 Edge Detector Performance = 176
  5.8.1 Methods for Evaluating Performance = 177
  5.8.2 Figure of Merit = 178
 5.9 Sequential Methods = 179
 5.10 Line Detection = 180
6 Contours = 186
 6.1 Geometry of Curves = 188
 6.2 Digital Curves = 188
  6.2.1 Chain Codes = 189
  6.2.2 Slope Representation = 191
  6.2.3 Slope Density Function = 191
 6.3 Curve Fitting = 192
 6.4 Polyline Representation = 194
  6.4.1 Polyline Splitting = 196
  6.4.2 Segment Merging = 196
  6.4.3 Split and Merge = 198
  6.4.4 Hop-Along Algorithm = 199
 6.5 Circular Arcs = 200
 6.6 Conic Sections = 203
 6.7 Spline Curves = 207
 6.8 Curve Approximation = 210
  6.8.1 Total Regression = 212
  6.8.2 Estimating Corners = 214
  6.8.3 Robust Regression = 214
  6.8.4 Hough Transform = 218
 6.9 Fourier Descriptors = 223
7 Texture = 234
 7.1 Introduction = 234
 7.2 Statistical Methods of Texture Analysis = 236
 7.3 Structural Analysis of Ordered Texture = 239
 7.4 Model-Based Methods for Texture Analysis = 240
 7.5 Shape From Texture = 241
8 Optics = 249
 8.1 Lens Equation = 250
 8.2 Image Resolution = 250
 8.3 Depth of Field = 251
 8.4 View Volume = 253
 8.5 Exposure = 254
9 Shading = 257
 9.1 Image Irradiance = 257
  9.1.1 Illumination = 259
  9.1.2 Reflectance = 261
 9.2 Surface Orientation = 264
 9.3 The Reflectance Map = 267
  9.3.1 Diffuse Reflectance = 267
  9.3.2 Scanning Electron Microscopy = 268
 9.4 Shape from Shading = 269
 9.5 Photometric Stereo = 271
10 Color = 276
 10.1 Color Physics = 276
 10.2 Color Terminology = 277
 10.3 Color Perception = 278
 10.4 Color Processing = 280
 10.5 Color Constancy = 284
 10.6 Discussion = 286
11 Depth = 289
 11.1 Stereo Imaging = 289
  11.1.1 Cameras in Arbitrary Position and Orientation = 291
 11.2 Stereo Matching = 293
  11.2.1 Edge Matching = 294
  11.2.2 Region Correlation = 295
 11.3 Shape from X = 298
 11.4 Range Imaging = 300
  11.4.1 Structured Lighting = 301
  11.4.2 Imaging Radar = 305
 11.5 Active Vision = 305
12 Calibration = 309
 12.1 Coordinate Systems = 311
 12.2 Rigid Body Transformations = 313
  12.2.1 Rotation Matrices = 316
  12.2.2 Axis of Rotation = 318
  12.2.3 Unit Quaternions = 318
 12.3 Absolute Orientation = 320
 12.4 Relative Orientation = 325
 12.5 Rectification = 331
 12.6 Depth from Binocular Stereo = 332
 12.7 Absolute Orientation with Scale = 334
 12.8 Exterior Orientation = 336
  12.8.1 Calibration Example = 340
 12.9 Interior Orientation = 341
 12.10 Camera Calibration = 346
  12.10.1 Simple Method for Camera Calibration = 347
  12.10.2 Affine Method for Camera Calibration = 352
  12.10.3 Nonlinear Method for Camera Calibration = 355
 12.11 Binocular Stereo Calibration = 357
 12.12 Active Triangulation = 359
 12.13 Robust Methods = 361
 12.14 Conclusions = 361
13 Curves and Surfaces = 365
 13.1 Ficlds = 366
 13.2 Geometry of Curves = 367
 13.3 Geometry of Surfaces = 369
  13.3.1 Planes = 369
  13.3.2 Differential Geometry = 370
 13.4 Curve Representations = 373
  13.4.1 Cubic Spline Curves = 373
 13.5 Surface Representations = 374
  13.5.1 Polygonal Meshes = 374
  13.5.2 Surface Patches = 378
  13.5.3 Tensor - Product Surfaces = 380
 13.6 Surface Interpolation = 381
  13.6.1 Triangular Mesh Interpolation = 381
  13.6.2 Bilinear Interpolation = 382
  13.6.3 Robust Interpolation = 384
 13.7 Surface Approximation = 385
  13.7.1 Regression Splines = 387
  13.7.2 Variational Methods = 395
  13.7.3 Weighted Spline Approximation = 395
 13.8 Surface Segmentation = 397
  13.8.1 Initial Segmentation = 398
  13.8.2 Extending Surface Patches = 399
 13.9 Surface Registration = 400
14 Dynamic Vision = 406
 14.1 Change Detection = 408
  14.1.1 Difference Pictures = 409
  14.1.2 Static Segmentation and Matching = 415
 14.2 Segmentation Using Motion = 416
  14.2.1 Time - Varying Edge Detection = 416
  14.2.2 Stationary Camera = 418
 14.3 Motion Correspondence = 420
 14.4 Image Flow = 428
  14.4.1 Computing Image Flow = 428
  14.4.2 Feature-Based Methods = 429
  14.4.3 Gradient-Based Methods = 429
  14.4.4 Variational Methods for Image Flow = 431
  14.4.5 Robust Computation of Image Flow = 432
  14.4.6 Information in Image Flow = 435
 14.5 Segmentation Using a Moving Camera = 436
  14.5.1 Ego-Motion Complex Log Mapping = 437
  14.5.2 Depth Determination = 439
 14.6 Tracking = 443
  14.6.1 Deviation Function for Path Coherence = 444
  14.6.2 Path Coherence Function = 445
  14.6.3 Path Coherence in the Presence of Occlusion = 447
  14.6.4 Modified Greedy Exchange Algorithm = 448
 14.7 Shape from Motion = 451
15 Object Recognition = 459
 15.1 System Components = 460
 15.2 Complexity of Object Recognition = 462
 15.3 Object Representation = 465
  15.3.1 Observer-Centered Representations = 466
  15.3.2 Object-Centered Representations = 467
 15.4 Feature Detection = 472
 15.5 Recognition Strategies = 473
  15.5.1 Classification = 474
  15.5.2 Matching = 479
  15.5.3 Feature Indexing = 481
 15.6 Verification = 481
  15.6.1 Template Matching = 482
  15.6.2 Morphological Approach = 483
  15.6.3 Symbolic = 483
  15.6.4 Analogical Methods = 486
A Mathematical Concepts = 492
 A.1 Analytic Geometry = 492
 A.2 Linear Algebra = 494
 A.3 Variational Calculus = 498
 A.4 Numerical Methods = 500
B Statistical Methods = 502
 B.1 Measurement Errors = 502
 B.2 Error Distributions = 504
 B.3 Linear Regression = 506
 B.4 Nonlinear Regression = 510
C Programming Techniques = 511
 C.1 Image Descriptors = 511
 C.2 Mapping Operators = 516
 C.3 Image File Formats = 517
Bibliography = 519
Index = 542