Image Modeling (eBook)

Azriel Rosenfeld (Herausgeber)

eBook Download: PDF

2014 | 1. Auflage
460 Seiten
Elsevier Science (Verlag)
9781483275604 (ISBN)

Infos

Image Modeling compiles papers presented at a workshop on image modeling in Rosemont, Illinois on August 6-7, 1979. This book discusses the mosaic models for textures, image segmentation as an estimation problem, and comparative analysis of line-drawing modeling schemes. The statistical models for the image restoration problem, use of Markov random fields as models of texture, and mathematical models of graphics are also elaborated. This text likewise covers the univariate and multivariate random field models for images, stochastic image models generated by random tessellations of the plane, and long crested wave models. Other topics include the Boolean model and random sets, structural basis for image description, and structure in co-occurrence matrices for texture analysis. This publication is useful to specialists and professionals working in the field of image processing.

Inhalt

Front Cover 1
Image Modeling 4
Copyright Page 5
Table of Contents 6
List of Contributors 10
Preface 14
Chapter 1. 
16 
1. MOSAIC MODELS 16
2. PROPERTIES OF MOSAIC MODELS 19
REFERENCES 22
Chapter 2. 
24 
1. INTRODUCTION 24
2. OVERALL STRATEGY 25
3. REGION TESTING 26
4. EVALUATION OF INTRINSIC BOUNDARY AMBIGUITY 35
5. APPROXIMATE BOUNDARY 38
6. EXPERIMENTAL RESULTS 39
7. CONCLUSIONS 42
REFERENCES 42
Chapter 3. Toward a Structural Textural Analyzer Based on Statistical Methods 44
1. INTRODUCTION 44
2. BACKGROUND 46
3. A MODEL FOR TEXTURE BASED ON MATHEMATICAL TILING 50
4. FORMALIZING THE CONCEPT OF PERIOD PARALLELOGRAM 
57 
5. FINDING THE SIZE, SHAPE, AND ORIENTATION OF A PERIOD PARALLELOGAM UNIT PATTERN OF APERIODIC TEXTURE 61
6. FURTHER PROPERTIES OF THE INERTIA MEASURE 68
7. CONCLUSIONS 74
REFERENCES 75
Chapter 4. 
78 
1. INTRODUCTION 78
2. BOUNDARY ESTIMATION AS LIKELIHOOD MAXIMIZATION 81
3. THE RIPPLE FILTER: A REGION GROWING/SHRINKING 
86 
4. SEQUENTIAL BOUNDARY FINDER 94
5. MORE ON BOUNDARY MODELS 98
6. BOUNDARY ERROR ESTIMATION 100
7. OBJECT RECOGNITION 104
8. COMMENTS 106
REFERENCES 108
Chapter 5. 
110 
1. INTRODUCTION 110
2. TEXTURE MODELS 111
3. A ONE-DIMENSIONAL EDGE DETECTOR 112
4. ANALYSIS OF ek 113
5. DISCUSSION 123
REFERENCES 123
Chapter 6. 
126 
1. INTRODUCTION 126
2. LINE-DRAWING QUANTIZATION 127
3. APPROXIMANTS 128
4. QUANTIZATION 131
5. LINK PROBABILITIES 135
6. EXPERIMENTAL RESULTS 142
7. CONCLUSION 144
APPENDIX : Line Segments in an m X n Lattice Field 144
REFERENCES 145
Chapter 7. 
148 
1. INTRODUCTION 148
2. AN OBJECT IS POSITIVE 150
3. SOME OBJECTS ARE BOUNDED ABOVE AND BELOW 153
4. SOME OBJECTS ARE POWER SPECTRA 155
5. THE RECONCILIATION MODEL OF KIKUCHI AND SOFFER 156
6. SOME OBJECTS ARE SIMPLY CONNECTED 158
7. AN OBJECT IS A PROBABILITY LAW: MAXIMUM INFORMATION RESTORATION 162
8. SUMMARY 166
REFERENCES 167
Chapter 8. 
168 
1. INTRODUCTION 168
2. TREE GRAMMARS AND STOCHASTIC TREE GRAMMARS 168
3. APPLICATION OF TREE GRAMMARS TO IMAGE MODELING 177
4. CONCLUDING REMARKS 182
REFERENCES 184
Chapter 9. 
186 
1. INTRODUCTION 186
2. THE SLOPED-FACET MODEL 187
3. SLOPED FACET PARAMETER ESTIMATION AND SIGNIFICANCE MEASURE 188
4. USING THE SLOPED-FACET MODEL 194
5. LITERATURE REVIEW 195
6. BAYESIAN EDGE DETECTION AND REGION ANALYSIS 197
7. CONCLUSION 199
REFERENCES 199
Chapter 10. 
200 
1. MRFS AS STATISTICAL MODELS OF TEXTURE 200
2. AN MRF SIMULATION ALGORITHM 206
3. MRF PARAMETER ESTIMATION 210
ACKNOWLEDGMENT 213
REFERENCES 213
Chapter 11. 
214 
1. IMAGE RECONSTRUCTION FROM PROJECTIONS 214
2. NOISE IN IMAGE RECONSTRUCTION 219
3. ILLUSTRATIONS 220
4. DISCUSSION 224
ACKNOWLEDGMENTS 228
REFERENCES 228
Chapter 12. 
230 
1. INTRODUCTION 230
2. MATHEMATICAL FRAMEWORK 230
3. JOINT PROBABILITY MODEL 231
4. CONDITIONAL PROBABILITY MODEL 233
5. CONTOUR MODELS 235
6. PATTERN RECOGNITION MODELS 236
7. DEPENDENCE ON RESOLUTION 237
8. PROSPECTS 237
ACKNOWLEDGMENT 237
REFERENCES 237
Chapter 13. 
240 
1. INTRODUCTION 240
2. CONVENTIONAL STATISTICAL IMAGE MODELS 241
3. NONSTATIONARY STATISTICAL IMAGE MODELS 243
4. TRANSFORMATION TO STATIONARY BEHAVIOR 245
5. A 
247 
6. EXAMPLES OF APPLICATIONS OF NONSTATIONARY MODELS 247
7. CLOSING REMARKS 253
REFERENCES 253
Chapter 14. 
254 
1. INTRODUCTION 254
2. THE MARKOV MESH 254
3. SOME COMMENTS ON RELATIONS TO OTHER CONTEMPORARY 
256 
ACKNOWLEDGMENT 258
REFERENCES 258
Chapter 15. 
260 
1. INTRODUCTION 260
2. PERIODIC UNIVARIATE RANDOM FIELD 261
3. PARAMETER ESTIMATION 265
4. CHOICE OF APPROPRIATE NEIGHBORS 266
5. SEGMENTATION OF AN IMAGE 267
6. IMAGE COMPRESSION AND RESTORATION 268
7. MULTIVARIATE RANDOM FIELD 269
8. CONCLUSIONS 271
APPENDIX 1 272
REFERENCES 273
Chapter 16. 
274 
1. INTRODUCTION 274
2. ELEMENTS OF PATTERN THEORY 274
3. GENERAL IMAGE MODELS 279
4. EXAMPLES 283
REFERENCES 290
Chapter 17. 
292 
1. INTRODUCTION 292
2. MOBILE GEOMETRICAL OBJECTS 293
3. MEASURES AND INVARIANT MEASURES 295
4. A SINGLE RANDOM MOBILE OBJECT 297
5. TWO OR MORE RANDOM MOBILE OBJECTS 300
6. TWO STANDARD TECHNIQUES 304
7. POISSON MODELS 307
8. EXPECTED NUMBERS AND ERGODIC DISTRIBUTIONS OF n-FIGURES 309
9. ASSORTED TOPICS 310
10. SQUARE LATTICE ANALOGS 313
REFERENCES 314
Chapter 18. 
316 
1. INTRODUCTION 316
2. PRELIMINARIES 317
3. CONSTRUCTION PROCEDURE 318
4. SECOND-ORDER PROPERTIES 327
5. APPLICATIONS 333
6. SUMMARY AND CONCLUSIONS 339
REFERENCES 339
Chapter 19. 
342 
1. INTRODUCTION 342
2. THE STANDARD MODEL 342
3. NARROW-BAND NOISE MODEL 348
4. FITTING THE MODEL TO REAL DATA 351
5. GEOMETRICAL PROPERTIES 352
6. DISCUSSION 355
REFERENCES 355
Chapter 20. 
358 
NOTATION 358
A COUNTERPOINT 359
1. CONSTRUCTION OF THE BOOLEAN SETS 360
1.* RANDOM SETS: DEFINITION AND BASIC PROPERTIES 361
2. THE FUNCTIONAL MOMENT OF THE BOOLEAN MODEL 363
2.* INFINITE DIVISIBILITY 366
3. CONVEX PRIMARY GRAINS 366
3.* SEMI-MARKOV RACS 368
4. CONNECTIVITY NUMBER 370
4.* DIGITIZATION 372
5. SPECIFIC BOOLEAN MODELS 374
5.* ESTIMATION PROBLEMS 375
6. DERIVED MODELS 378
6.* THE ROSE OF MODELS 384
REFERENCES 384
Chapter 21. 
386 
1. INTRODUCTION 386
2. STATISTICAL VERSUS STRUCTURAL COMPLEXITY 387
3. NECESSITY FOR STRUCTURAL MODELS 387
4. A PARADIGM FOR STRUCTURAL MODELING 388
5. DISCUSSION 403
ACKNOWLEDGMENTS 404
REFERENCES 404
Chapter 22. 
406 
1. INTRODUCTION 406
2. TWO-DIMENSIONAL TIME SERIES MODEL 408
3. G MATRIX EIGENVALUE APPROACH 421
4. PIXEL-VECTOR CLUSTERING TECHNIQUE 432
ACKNOWLEDGMENT 435
REFERENCES 435
Chapter 23. 
438 
1. INTRODUCTION 438
2. CO-OCCURRENCE MATRICES FOR TEXTURE CLASSIFICATION 439
3. CONTINGENCY TABLES AND x2 SIGNIFICANCE TESTS 440
4. AN 
445 
5. EXPERIMENTS 449
6. DISCUSSION AND CONCLUSIONS 454
APPENDIX: ALINEAR DISCRIMINANT CLASSIFIER 456
REFERENCES 459

Produkt-Details

• Erscheint lt. Verlag: 10.5.2014
• Sprache: englisch
• Themenwelt: Informatik ► Grafik / Design ► Digitale Bildverarbeitung
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik
• ISBN-13: 9781483275604 / 9781483275604