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CIS 601 Image Fundamentals

CIS 601 Image Fundamentals. Dr. Rolf Lakaemper. Fundamentals. Parts of these slides base on the textbook Digital Image Processing by Gonzales/Woods Chapters 1 / 2. Fundamentals. These slides show basic concepts about digital images. Fundamentals. Let’s have a look at the human eye.

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CIS 601 Image Fundamentals

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  1. CIS 601 Image Fundamentals Dr. Rolf Lakaemper

  2. Fundamentals Parts of these slides base on the textbook Digital Image Processing by Gonzales/Woods Chapters 1 / 2

  3. Fundamentals These slides show basic concepts about digital images

  4. Fundamentals Let’shave a look at the human eye

  5. Fundamentals

  6. Fundamentals • We are mostly interested in the retina: • consists of cones and rods • Cones • color receptors • About 7 million, primarily in the retina’s central portion • for image details • Rods • Sensitive to illumination, not involved in color vision • About 130 million, all over the retina • General, overall view

  7. Fundamentals • The human eye • Is able to perceive electromagnetic waves in a certain spectrum • Is able to distinguish between wavelengths in this spectrum (colors) • Has a higher density of receptors in the center • Maps our 3D reality to a 2 dimensional image !

  8. Fundamentals …or more precise: maps our continous (?) reality to a (spatially) DISCRETE 2D image

  9. Fundamentals • Some topics we have to deal with: • Sharpness • Brightness • Processing of perceived visual information

  10. Fundamentals Sharpness The eye is able to deal with sharpness in different distances

  11. Fundamentals Brightness The eye is able to adapt to different ranges of brightness

  12. Fundamentals Processing of perceived information: optical illusions

  13. Fundamentals optical illusions: Digital Image Processing does NOT (primarily) deal with cognitive aspects of the perceived image !

  14. Fundamentals What is an image ?

  15. Fundamentals The retinal model is mathematically hard to handle (e.g. neighborhood ?)

  16. Fundamentals Easier: 2D array of cells, modelling the cones/rods Each cell contains a numerical value (e.g. between 0-255)

  17. Fundamentals • The position of each cell defines the position of the receptor • The numerical value of the cell represents the illumination received by the receptor 5 7 1 0 12 4 … … …

  18. Fundamentals • With this model, we can create GRAYVALUE images • Value = 0: BLACK (no illumination / energy) • Value = 255: White (max. illumination / energy)

  19. Fundamentals A 2D grayvalue - image is a 2D -> 1D function, v = f(x,y)

  20. Fundamentals As we have a function, we can apply operators to this function, e.g. H(f(x,y)) = f(x,y) / 2 Operator Image (= function !)

  21. Fundamentals H(f(x,y)) = f(x,y) / 2 6 8 2 0 3 4 1 0 12 200 20 10 6 100 10 5

  22. Fundamentals Remember: the value of the cells is the illumination (or brightness) 6 8 2 0 3 4 1 0 12 200 20 10 6 100 10 5

  23. Fundamentals The mandatory steps: Image Acquisition and Representation

  24. Fundamentals Acquisition

  25. Fundamentals Acquisition

  26. Fundamentals • Typical sensor for images: • CCD Array (Charge Couple Devices) • Use in digital cameras • Typical resolution 1024 x 768 (webcam)

  27. Fundamentals CCD

  28. Fundamentals CCD

  29. Fundamentals CCD (3.2 million pixels)

  30. Fundamentals Representation The Braun Tube

  31. Fundamentals Representation Black/White and Color

  32. Fundamentals Color Representation: Red / Green / Blue Model for Color-tube Note: RGB is not the ONLY color-model, in fact its use is quiet restricted. More about that later.

  33. Fundamentals Color images can be represented by 3D Arrays (e.g. 320 x 240 x 3)

  34. Fundamentals But for the time being we’ll handle 2D grayvalue images

  35. Fundamentals Digital vs. Analogue Images Analogue: Function v = f(x,y): v,x,y are REAL Digital: Function v = f(x,y): v,x,y are INTEGER

  36. Fundamentals Stepping down from REALity to INTEGER coordinates x,y: Sampling

  37. Fundamentals Stepping down from REALity to INTEGER grayvalues v : Quantization

  38. Fundamentals Sampling and Quantization

  39. Fundamentals MATLAB demonstrations of sampling and quantization effects

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