Algorithms for Image Processing and Computer Vision

✍ Scribed by Parker, James R

• Publisher: Wiley
• Year: 2010
• Tongue: English
• Leaves: 506
• Edition: 2
• Category: Library

✦ Synopsis

A cookbook of algorithms for common image processing applicationsThanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. It's an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processing.


Algorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialists This bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aids Saves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applications.Algorithms for Image Processing and Computer Vision, 2nd Editionprovides the tools to speed development of image processing applications.

✦ Table of Contents

Shader Programming Basics......Page 3
Contents......Page 15
Preface......Page 23
Chapter 1 Practical Aspects of a Vision System — Image Display, Input/Output, and Library Calls......Page 27
The Basic OpenCV Code......Page 28
The IplImage Data Structure......Page 29
Reading and Writing Images......Page 32
An Example......Page 33
Image Capture......Page 36
Interfacing with the AIPCV Library......Page 40
References......Page 44
The Purpose of Edge Detection......Page 47
Traditional Approaches and Theory......Page 49
Models of Edges......Page 50
Noise......Page 52
Derivative Operators......Page 56
Template-Based Edge Detection......Page 62
Edge Models: The Marr-Hildreth Edge Detector......Page 65
The Canny Edge Detector......Page 68
The Shen-Castan (ISEF) Edge Detector......Page 74
A Comparison of Two Optimal Edge Detectors......Page 77
Color Edges......Page 79
Source Code for the Marr-Hildreth Edge Detector......Page 84
Source Code for the Canny Edge Detector......Page 88
Source Code for the Shen-Castan Edge Detector......Page 96
Website Files......Page 106
References......Page 108
Morphology Defined......Page 111
Connectedness......Page 112
Elements of Digital Morphology—Binary Operations......Page 113
Binary Dilation......Page 114
Implementing Binary Dilation......Page 118
Binary Erosion......Page 120
Implementation of Binary Erosion......Page 126
Opening and Closing......Page 127
MAX—A High-Level Programming Language for Morphology......Page 133
The ‘‘Hit-and-Miss’’ Transform......Page 139
Conditional Dilation......Page 142
Counting Regions......Page 145
Grey-Level Morphology......Page 147
Opening and Closing......Page 149
Smoothing......Page 152
Gradient......Page 154
Segmentation of Textures......Page 155
Size Distribution of Objects......Page 156
Color Morphology......Page 157
Website Files......Page 158
References......Page 161
Basics of Grey-Level Segmentation......Page 163
Using Edge Pixels......Page 165
Iterative Selection......Page 166
The Method of Grey-Level Histograms......Page 167
Using Entropy......Page 168
Fuzzy Sets......Page 172
Minimum Error Thresholding......Page 174
Sample Results From Single Threshold Selection......Page 175
The Use of Regional Thresholds......Page 177
Chow and Kaneko......Page 178
Modeling Illumination Using Edges......Page 182
Implementation and Results......Page 185
Comparisons......Page 186
Relaxation Methods......Page 187
Moving Averages......Page 193
Cluster-Based Thresholds......Page 196
Multiple Thresholds......Page 197
Website Files......Page 198
References......Page 199
Texture and Segmentation......Page 203
A Simple Analysis of Texture in Grey-Level Images......Page 205
Grey-Level Co-Occurrence......Page 208
Homogeneity......Page 211
Speeding Up the Texture Operators......Page 212
Edges and Texture......Page 214
Energy and Texture......Page 217
Vector Dispersion......Page 219
Surface Curvature......Page 221
Fractal Dimension......Page 224
Color Segmentation......Page 227
Website Files......Page 231
References......Page 232
What Is a Skeleton?......Page 235
The Medial Axis Transform......Page 236
Iterative Morphological Methods......Page 238
The Use of Contours......Page 247
Choi/Lam/Siu Algorithm......Page 250
Treating the Object as a Polygon......Page 252
Triangulation Methods......Page 253
Force-Based Thinning......Page 254
Definitions......Page 255
Use of a Force Field......Page 256
Subpixel Skeletons......Page 260
Source Code for Zhang-Suen/Stentiford/Holt Combined Algorithm......Page 261
Website Files......Page 272
References......Page 273
Image Degradations—The Real World......Page 277
The Frequency Domain......Page 279
The Fourier Transform......Page 280
The Fast Fourier Transform......Page 282
Two-Dimensional Fourier Transforms......Page 286
Fourier Transforms in OpenCV......Page 288
Creating Artificial Blur......Page 290
The Inverse Filter......Page 296
The Wiener Filter......Page 297
Structured Noise......Page 299
Motion Blur—A Special Case......Page 302
The Homomorphic Filter—Illumination......Page 303
Frequency Filters in General......Page 304
Isolating Illumination Effects......Page 306
Website Files......Page 307
References......Page 309
Objects, Patterns, and Statistics......Page 311
Features and Regions......Page 314
Training and Testing......Page 318
Variation: In-Class and Out-Class......Page 321
Minimum Distance Classifiers......Page 325
Distance Metrics......Page 326
Distances Between Features......Page 328
Cross Validation......Page 330
Support Vector Machines......Page 332
Merging Multiple Methods......Page 335
Merging Type 1 Responses......Page 336
Evaluation......Page 337
Converting Between Response Types......Page 338
Merging Type 2 Responses......Page 339
Bagging......Page 341
Boosting......Page 342
Website Files......Page 343
References......Page 344
The Problem......Page 347
OCR on Simple Perfect Images......Page 348
OCR on Scanned Images—Segmentation......Page 352
Noise......Page 353
Isolating Individual Glyphs......Page 355
Matching Templates......Page 359
Statistical Recognition......Page 363
OCR on Fax Images—Printed Characters......Page 365
Orientation—Skew Detection......Page 366
The Use of Edges......Page 371
Handprinted Characters......Page 374
Properties of the Character Outline......Page 375
Convex Deficiencies......Page 379
Vector Templates......Page 383
Neural Nets......Page 389
A Simple Neural Net......Page 390
A Backpropagation Net for Digit Recognition......Page 394
Merging Multiple Methods......Page 398
Printed Music Recognition—A Study......Page 401
Staff Lines......Page 402
Segmentation......Page 404
Music Symbol Recognition......Page 407
Source Code for Neural Net Recognition System......Page 409
Website Files......Page 416
References......Page 418
Searching Images......Page 421
Maintaining Collections of Images......Page 422
Color Image Features......Page 425
Color Quad Tree......Page 426
Hue and Intensity Histograms......Page 427
Comparing Histograms......Page 428
Requantization......Page 429
Results from Simple Color Features......Page 430
Other Color-Based Methods......Page 433
Grey-Level Image Features......Page 434
Edge Density—Boundaries Between Objects......Page 435
Boolean Edge Density......Page 436
Overall Regions......Page 437
Angular Regions......Page 438
Test of Spatial Sampling......Page 440
Additional Considerations......Page 443
Data Sets......Page 444
Website Files......Page 445
References......Page 446
Systems......Page 450
Chapter 11 High-Performance Computing for Vision and Image Processing......Page 451
Shared Memory......Page 452
Execution Timing......Page 453
Using clock()......Page 454
Using QueryPerformanceCounter......Page 456
Installing MPI......Page 458
Using MPI......Page 459
Inter-Process Communication......Page 460
Running MPI Programs......Page 462
Real Image Computations......Page 463
Using a Computer Network—Cluster Computing......Page 466
GLSL......Page 470
OpenGL Fundamentals......Page 471
Practical Textures in OpenGL......Page 474
Vertex and Fragment Shaders......Page 478
Required GLSL Initializations......Page 479
Reading and Converting the Image......Page 480
Passing Parameters to Shader Programs......Page 482
Putting It All Together......Page 483
Developing and Testing Shader Code......Page 485
Finding the Needed Software......Page 486
References......Page 487
Index......Page 491

✦ Subjects

Science;Computer Science;Programming;Algorithms