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Digital Image Fundamentals

Digital Image Fundamentals. Human Vision Lights and Electromagnetic spectrum Image Sensing & Acquisition Sampling & Quantization Basic Relationships b/w Pixels. Important dates 9/29: Project grouping (2~3 members/group) 10/6: First image processing GUI due!! OpenCV ImageMagick ImageJ

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Digital Image Fundamentals

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  1. Digital Image Fundamentals Human Vision Lights and Electromagnetic spectrum Image Sensing & Acquisition Sampling & Quantization Basic Relationships b/w Pixels

  2. Important dates • 9/29: Project grouping (2~3 members/group) • 10/6: First image processing GUI due!! • OpenCV • ImageMagick • ImageJ • Ximage or ImageX Digital Image Processing

  3. A Cross Section of the Human Eye • Iris – 虹膜 • Lens – 水晶體 • Cornea – 角膜 • Sclera – 鞏膜 • Choroid – 脈絡膜 • Retina – 視網膜 • Fovea – 視乳頭 • Ciliary body – 睫狀體 Digital Image Processing

  4. Human Vision • Rods: 108 • Shape/form perception • Large dynamic range • Limited contrast • Scotopic (dim-light) vision • Cones 5 X 106 • 3-channel color perception • Photopic (bright-light) vision Digital Image Processing

  5. Distribution of Rods and Cones in the Retina Digital Image Processing

  6. Image Formation in the Eye Digital Image Processing

  7. Range of Subjective Brightness • Visual system cannot operate over full range of subjective brightness simultaneous • Via brightness adaptation Digital Image Processing

  8. Brightness Discrimination • ∆Ic – increment of illumination discriminable 50% of the time I • Small ∆Ic/I => good discrimination; otherwise, poor. Weber ratio / intensity Digital Image Processing

  9. Perceived Brightness • Two phenomena • Undershoot or overshoot around the boundaries; Mach band pattern • Simultaneous contrast Digital Image Processing

  10. Optical Illusions Digital Image Processing

  11. Electromagnetic Spectrum Digital Image Processing

  12. Image Sensing • Single sensor • Sensor strip • Sensor array Digital Image Processing

  13. A Simple Image Model • i(x,y)– illumination (from light source) • r(x,y)– reflectance of illuminated surface (reflectivity) • Lambertian surface • Looks the same in all directions • Specular (mirror-like) surface • Incidence angle = reflectance angle Digital Image Processing

  14. A Simple Image Model (continued) • f(x,y) = i(x,y) X r(x,y) >= 0 • r(x,y) • 0.93 white snow • 0.01 black velvet • i(x,y) • 9000 foot-candle Sun • 0.01 foot-candle full moon Digital Image Processing

  15. Sampling & Quantization Digital Image Processing

  16. A Digital Image of MXN Array Digital Image Processing

  17. A Digital Image (continued) • Image Sampling – Spatial-coordinate digitization • Gray-level Quantization – amplitude digitization • N = size of image = (number of columns) X (number of rows) • G (number of gray levels) = 2k • Disk storage needed = N * ceiling(k/8) Digital Image Processing

  18. Storage Bits for N and k Digital Image Processing

  19. Spatial Resolution Digital Image Processing

  20. Amplitude Quantization Digital Image Processing

  21. Level of Detail (LOD) Low level of detail           High level of detail Digital Image Processing

  22. Isopreference Digital Image Processing

  23. Scaling and Interpolation Digital Image Processing

  24. Basic Image Topology • Neighbors of a Pixel • 4-neighbor and 8-neighbor • 4-adjacent and 8-adjacent • Connectivity • 4-connectivity • 8-connectivity • M-connectivity (mixed connectivity) Digital Image Processing

  25. M-Connectivity Digital Image Processing

  26. Further Pixel Relationships • Connected Component Labeling • Relations, Equivalence, and Transitive Closure • Distance Measures • Arithmetic/Logic Operations • Mask Operations Digital Image Processing

  27. Logic Mask Operations Digital Image Processing

  28. Weighted Mask Operation Digital Image Processing

  29. Utilizing ALU Parallel Processing Digital Image Processing

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