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基礎影像處理

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basic image processing
2014 summer course

Veröffentlicht in: Ingenieurwesen
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基礎影像處理

  1. 1. 基礎影像處理! Basic Image Processing 2014應⽤用輔具系統暑期課程! 鄭煒翰
  2. 2. OUTLINE 1. Image Basic Concepts! 2. Image Processing! 3. Lab
  3. 3. • Image Basic Concepts! Image! Color! • Image processing! • Lab
  4. 4. • Bitmap! • Vector
  5. 5. • Single Sensor (point)! • Sensor Stripes (line)! • Sensor Array (Array)
  6. 6. • dpi (dots per inch) for printer! • ppi (pixel per inch) for display
  7. 7. • color depth (bit depth)! monochrome (black & white)! gray-scale! 16 colors (4 bits)! 256 colors (8 bits)! high color (15/16 bits)! true color (24 bits) ! • color model
  8. 8. • 1 bit! • black & white (B&W)! 0 & 1
  9. 9. • 8 bits! • 256 different intensities of gray! • the result of measuring the intensity of light
  10. 10. • 4 bits ! • Red, Green, Blue, Intensity Microsoft Apple
  11. 11. 1. Indexed Color (palette)! 2. RRRGGGBB (8 bits) 3-3-2 bit RGB
  12. 12. • 4, 16, 256(at most)! • to manage colors of images! • to save memory & storage! • speed up display refresh ! ! • the most representative colors, or the fixed hardware colors, are grouped into a limited size palette: an array of color elements
  13. 13. File size =! 6x3 bytes (palette) ! + 7x5x1 bytes (indices) ! ! ! ! ! = 53 bytes File size =! ! ! 7x5x3 bytes! ! ! ! ! = 105 bytes
  14. 14. • 15-bit high color! • 16-bit high color! 65,536 colors 15 bits 16 bits
  15. 15. • 24 bits! 256 × 256 × 265 = 16,777,216 colors! ! ! • 32 bits 24 bits
  16. 16. • color depth! • color model! RGB! CMYK! YIQ! YUV! HSV & HSL
  17. 17. • RGB! RGB 565! RGB 888! • CMYK! for print! ! • NOT intuition
  18. 18. • YIQ! Y provide the brightness of TV signals (Luminance)! I (In-phase),Q (Quadrature-phase)! RGB to YIQ! • Y = 0.299R + 0.587G + 0.114B! • I = 0.596R - 0.274G - 0.322B! • Q = 0.211R - 0.523G + 0.312B
  19. 19. • YUV - Y(Luminance), U(Chrominance), V(Chroma)! focus on the sensitivity of the lightness of vision! YUV 444 (3 bytes per pixel)! YUV 422 (4 bytes per 2 pixels)! YUV 420 (6 bytes per 4 pixels)! RGB to YUV! • Y = 0.299R + 0.587G + 0.114B! • U = -0.147R - 0.289G + 0.436B! • V = 0.615R - 0.515G - 0.100B
  20. 20. • YUV! YUV 444 ! Y3 Y2 Y1 Y0 U3 U2 U1 U0 V3 V2 V1 V0! (Y3 U3 V3) (Y2 U2 V2) (Y1 U1 V1) (Y0 U0 V0)! Per subsample (1+1+1) = 3 bytes
  21. 21. • YUV! YUV 422 ! Y3 Y2 Y1 Y0 U1 U0 V1 V0! (Y3  1/2U1  1/2V1 ) (Y2  1/2U1  1/2V1 ) (Y1  1/2U0  1/2V0 ) (Y0  1/2U0  1/2V0)! Per subsample (1 + 0.5 + 0.5) = 2 bytes
  22. 22. • YUV! YUV 420 ! Y3 Y2 Y1 Y0 U0 V0! (Y3  1/4U0  1/4V0 ) (Y2  1/4U0  1/4V0 ) (Y1  1/4U0  1/4V0 ) (Y0  1/4U0  1/4V0)! Per subsample (1 + 0.25 + 0.25) = 1.5 bytes
  23. 23. • HSV(Hue, Saturation, Value) or HSB(B, Brightness)! • HSL(Hue, Saturation, Lightness) or HLS
  24. 24. • Image Basic Concepts! • Image Processing! Edge Detection! Histogram! Noise Removal! Threshold! • Lab
  25. 25. Image Processing Processing Recognition Computer Vision
  26. 26. • Edge! • Laplacian! • Sobel! • Prewitt! • Canny
  27. 27. • Edge! • derivative
  28. 28. • derive from 2nd derivative
  29. 29. • 1st derivative
  30. 30. • 1st derivative
  31. 31. • Double threshold! • computation : sobel > canny! • result : canny > sobel
  32. 32. • probability density function (pdf)! • cumulative distribution function (CDF)! • equalization
  33. 33. • histogram equalization
  34. 34. • Smoothing Method! ! ! ! • Median Method 1,1,2,2,2,2,3,3,200
  35. 35. • Before Recognition! After edge detection! • grayscale image! • image size is still large! Image Segmentation! • binary image (black & white)! threshold
  36. 36. Histogram! ! histogram equalization Edge! Detection! laplacian sobel prewitt Threshold! set threshold Noise! Removal! smoothing median Gray level! RGB to Gray
  37. 37. • Image Basic Concepts! • Image Processing! • Lab 38

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