Lehrveranstaltungsinhalt aus - Institute for Computer Graphics and ...

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4 CONTENTS 2 Sensing 51 2.1 The Most Important Sensors: The Eye and the Camera . . . . . . . . . . . . . . . 51 2.2 What is a Sensor Model? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.3 Image Scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.4 The Quality of Scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 2.5 Non-Perspective Cameras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 2.6 Heat Images or Thermal Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.7 Multispectral Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 2.8 Sensors to Image the Inside of Humans . . . . . . . . . . . . . . . . . . . . . . . . . 58 2.9 Panoramic Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.10 Making Images Independent of Sunlight and in Any Weather: Radar Images . . . . 59 2.11 Making Images with Sound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 2.12 Passive Radiometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 2.13 Microscopes and Endoscopes Imaging . . . . . . . . . . . . . . . . . . . . . . . . . 61 2.14 Objects-Scanners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 2.15 Photometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 2.16 Data Garments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 2.17 Sensors for Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 2.18 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3 Raster-Vector-Raster Convergence 69 3.1 Drawing a straight line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.2 Filling of Polygons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.3 Thick lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 3.4 The Transition from Thick Lines to Skeletons . . . . . . . . . . . . . . . . . . . . . 73 4 Morphology 79 4.1 What is Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.2 Dilation and Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3 Opening and Closing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 4.4 Morphological Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.5 Shape Recognition by a Hit or Miss Operator . . . . . . . . . . . . . . . . . . . . . 85 4.6 Some Additional Morphological Algorithms . . . . . . . . . . . . . . . . . . . . . . 86
CONTENTS 5 5 Color 93 5.1 Gray Value Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.2 Color images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.3 Tri-Stimulus Theory, Color Definitions, CIE-Model . . . . . . . . . . . . . . . . . . 96 5.4 Color Representation on Monitors and Films . . . . . . . . . . . . . . . . . . . . . 99 5.5 The 3-Dimensional Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.6 CMY-Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.7 Using CMYK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.8 HSI-Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5.9 YIQ-Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5.10 HSV and HLS -Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5.11 Image Processing with RGB versus HSI Color Models . . . . . . . . . . . . . . . . 110 5.12 Setting Colors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.13 Encoding in Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.14 Negative Photography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 5.15 Printing in Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 5.16 Ratio Processing of Color Images and Hyperspectral Images . . . . . . . . . . . . . 113 6 Image Quality 121 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.3 Gray Value and Gray Value Resolutions . . . . . . . . . . . . . . . . . . . . . . . . 121 6.4 Geometric Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.5 Geometric Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.6 Histograms as a Result of Point Processing or Pixel Processing . . . . . . . . . . . 123 7 Filtering 133 7.1 Images in the Spatial Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 7.2 Low-Pass Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 7.3 The Frequency Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7.4 High Pass-Filter - Sharpening Filters . . . . . . . . . . . . . . . . . . . . . . . . . . 137 7.5 The Derivative Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 7.6 Filtering in the Spectral Domain / Frequency Domain . . . . . . . . . . . . . . . . 140 7.7 Improving Noisy Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 7.8 The Ideal and the Butterworth High-Pass Filter . . . . . . . . . . . . . . . . . . . . 141 7.9 Anti-Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 7.9.1 What is Aliasing ? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 7.9.2 Aliasing by Cutting-off High Frequencies . . . . . . . . . . . . . . . . . . . . 142 7.9.3 Overcoming Aliasing with an Unweightable Area Approach . . . . . . . . . 143 7.9.4 Overcoming Aliasing with a Weighted Area Approach . . . . . . . . . . . . 143
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70 CHAPTER 3. RASTER-VECTOR-RASTER
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80 CHAPTER 4. MORPHOLOGY Definition
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92 CHAPTER 4. MORPHOLOGY
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94 CHAPTER 5. COLOR A monitor prese
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96 CHAPTER 5. COLOR Color in image
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98 CHAPTER 5. COLOR 3. Any point in
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102 CHAPTER 5. COLOR Antwort: es is
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104 CHAPTER 5. COLOR Algorithm 11 C
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120 CHAPTER 5. COLOR
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122 CHAPTER 6. IMAGE QUALITY Resolv
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124 CHAPTER 6. IMAGE QUALITY Algori
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130 CHAPTER 6. IMAGE QUALITY Slide
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132 CHAPTER 6. IMAGE QUALITY
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134 CHAPTER 7. FILTERING 7.2 Low-Pa
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136 CHAPTER 7. FILTERING 7.3 The Fr
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140 CHAPTER 7. FILTERING 9 9 8 8 6
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142 CHAPTER 7. FILTERING appearance
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144 CHAPTER 7. FILTERING Getting An
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146 CHAPTER 7. FILTERING
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148 CHAPTER 7. FILTERING Slide 7.29
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150 CHAPTER 7. FILTERING
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152 CHAPTER 8. TEXTURE µ n (z) =
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154 CHAPTER 8. TEXTURE Algorithm 23
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156 CHAPTER 8. TEXTURE now need to
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160 CHAPTER 8. TEXTURE
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162 CHAPTER 9. TRANSFORMATIONS In t
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196 CHAPTER 10. DATA STRUCTURES the
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212 CHAPTER 11. 3-D OBJECTS AND SUR
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224 CHAPTER 12. INTERACTION OF LIGH
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236 CHAPTER 13. STEREOPSIS entsprec
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240 CHAPTER 13. STEREOPSIS verify t
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242 CHAPTER 13. STEREOPSIS
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246 CHAPTER 14. CLASSIFICATION Prü
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252 CHAPTER 14. CLASSIFICATION
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256 CHAPTER 15. RESAMPLING images,
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268 CHAPTER 17. MOTION
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270 CHAPTER 18. MAN-MACHINE-INTERFA
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272 CHAPTER 19. PIPELINES 19.3 Revi
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274 CHAPTER 19. PIPELINES
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276 CHAPTER 20. IMAGE REPRESENTATIO
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286 APPENDIX A. ALGORITHMEN UND DEF
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288 APPENDIX A. ALGORITHMEN UND DEF
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290 APPENDIX B. FRAGENÜBERSICHT
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294 APPENDIX B. FRAGENÜBERSICHT 12
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300 APPENDIX B. FRAGENÜBERSICHT (a
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310 APPENDIX B. FRAGENÜBERSICHT R
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312 APPENDIX B. FRAGENÜBERSICHT A
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314 APPENDIX B. FRAGENÜBERSICHT y
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316 APPENDIX B. FRAGENÜBERSICHT gr
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330 APPENDIX B. FRAGENÜBERSICHT B
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344 APPENDIX B. FRAGENÜBERSICHT zu
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350 APPENDIX B. FRAGENÜBERSICHT ¡
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352 INDEX Entzerrung, 287 Erosion,
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354 INDEX Skalierung, 157, 303 Sobe
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356 LIST OF ALGORITHMS 29 z-buffer
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358 LIST OF DEFINITIONS 29 Boundary
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360 LIST OF FIGURES B.12 Lineare Tr
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362 LIST OF FIGURES
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