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Digital Image Processing

بسمه‌تعالي. Digital Image Processing. C o l o r Image Processing (Chapter 6). H.R. Pourreza. Preview. Motive - Color is a powerful descriptor that often simplifies object identification and extraction from a scene.

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Digital Image Processing

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  1. بسمه‌تعالي Digital Image Processing Color Image Processing(Chapter 6) H.R. Pourreza H.R. Pourreza

  2. Preview • Motive - Color is a powerful descriptor that often simplifies object identification and extraction from a scene. - Human can discern thousands of color shades and intensities, compared to about only two dozen shades of gray. H.R. Pourreza

  3. Preview H.R. Pourreza

  4. Preview H.R. Pourreza

  5. Preview • Color image processing is divide into two major area: • Full-Color Processing • Pseudo-Color Processing H.R. Pourreza

  6. Color Fundamentals The experiment of Sir Isaac Newton, in 1666. H.R. Pourreza

  7. Color Fundamentals (con’t)c H.R. Pourreza

  8. Color Fundamentals (con’t)c • Basic quantities to describe the quality of light source: • Radiance: Total amount of energy that flows from the light source (in W). • Luminance: A measure of the amount of energy an observer perceives from the light source (in lm) • Brightness: A subjective descriptor that embodies the achromatic notion of intensity and is practical impossible to measure. H.R. Pourreza

  9. Color Fundamentals (con’t) Standard wavelength values for the primary colors H.R. Pourreza

  10. Color Fundamentals (con’t) H.R. Pourreza

  11. Color Fundamentals (con’t) • The characteristics generally used to distinguish one color from another are Brightness,Hue, and Saturation. • Hue: Represents dominant color as perceive by an observer. • Saturation: Relative purity or the amount of white light mixed with a hue • Hue and saturation taken together are called Chromaticity, and therefore, a color may be characterized by its Brightness and Chromaticity. H.R. Pourreza

  12. Color Fundamentals (con’t) • Tri-stimulus values: The amount of Red, Green and Blue needed to form any particular color Denoted by: X, Y and Z • Tri-chromatic coefficient: H.R. Pourreza

  13. Color Fundamentals (con’t) Chromaticity Diagram Green Point = 62% green, 25% red, 13% blue. H.R. Pourreza

  14. Color Fundamentals (con’t) Color Gamut produced by RGB monitors Color Gamut produced by high quality color printing device H.R. Pourreza

  15. Color Models • The purpose of a color model (also called color space or color system) is to facilitate the specification of colors in some standard, generally accept way. • RGB(red,green,blue) : monitor, video camera. • CMY(cyan,magenta,yellow),CMYK (CMY, black) model for color printing. • and HSI model,which corresponds closely with the way humans describe and interpret color. H.R. Pourreza

  16. The RGB Color Models H.R. Pourreza

  17. The RGB Color Models (con’t) H.R. Pourreza

  18. The RGB Color Models (con’t) H.R. Pourreza

  19. The RGB Color Models (con’t) Safe RGB Colors (Safe Web colors) H.R. Pourreza

  20. The RGB Color Models (con’t) H.R. Pourreza

  21. The CMY and CMYK Color Models • Cyan, Magenta and Yellow are the secondary colors of light • Most devices that deposit colored pigments on paper, such as color printers and copiers, require CMY data input. H.R. Pourreza

  22. The HSI Color Models H.R. Pourreza

  23. The HSI Color Models H.R. Pourreza

  24. The HSI Color Models H.R. Pourreza

  25. The HSI Color Models • Converting colors from RGB to HSI H.R. Pourreza

  26. The HSI Color Models • Converting colors from HIS to RGB • RG sector : H.R. Pourreza

  27. The HSI Color Models • Converting colors from HIS to RGB • GB sector : H.R. Pourreza

  28. The HSI Color Models • Converting colors from HIS to RGB • BR sector : H.R. Pourreza

  29. The HSI Color Models H.R. Pourreza

  30. The HSI Color Models RGB H H S S I RGB I H.R. Pourreza

  31. Pseudocolor Image Processing • Pseudocolor (also called false color) image processing consists of assigning colors to gray values based on a specified criterion. • The principal use of pseudocolor is for human visualization and interpretation of gray-scale events in an image or sequence of images. H.R. Pourreza

  32. Intensity Slicing H.R. Pourreza

  33. Intensity Slicing (con’t) H.R. Pourreza

  34. Intensity Slicing (con’t) H.R. Pourreza

  35. Intensity Slicing (con’t) H.R. Pourreza

  36. Gray Level to Color Transformations H.R. Pourreza

  37. Gray Level to Color Transformations H.R. Pourreza

  38. Gray Level to Color Transformations H.R. Pourreza

  39. Gray Level to Color Transformations H.R. Pourreza

  40. Gray Level to Color Transformations H.R. Pourreza

  41. Gray Level to Color Transformations H.R. Pourreza

  42. Basic of Full Color Image Processing Let c represent an arbitrary vector in RGB color space For an image of size M*N, H.R. Pourreza

  43. Basic of Full Color Image Processing H.R. Pourreza

  44. Basic of Full-Color Image Processing • Major categories of full-color Image processing: • Per-color-component processing • Vector-based processing H.R. Pourreza

  45. Basic of Full-Color Image ProcessingColor Transformation Processing the components of a color image within the context of a single color model. Color components of g Color components of f Color mapping functions H.R. Pourreza

  46. Full-Color Image ProcessingColor Transformation CMYK • Some difficulty in interpreting the HUE: • Discontinuity where 0 and 360º meet. • Hue is undefined for a saturation 0 RGB HSI H.R. Pourreza

  47. Full-Color Image ProcessingColor Transformation: Modify the Intensity H.R. Pourreza

  48. Full-Color Image ProcessingColor Transformation: Color Complement H.R. Pourreza

  49. Full-Color Image ProcessingColor Transformation: Color Complement H.R. Pourreza

  50. Full-Color Image ProcessingColor Transformation: Color Slicing Motive: Highlighting a specific range of colors in an image • Basic Idea: • Display the color of interest so that they stand out from background • Use the region defined by the colors as a mask for further processing H.R. Pourreza

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