basic image processing 2014 summer course

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. OUTLINE
1. Image Basic Concepts!
2. Image Processing!
3. Lab

3. • Image Basic Concepts!
Image!
Color!
• Image processing!
• Lab

4. • Bitmap!
• Vector

5. • Single Sensor (point)!
• Sensor Stripes (line)!
• Sensor Array (Array)

6. • dpi (dots per inch) for printer!
• ppi (pixel per inch) for display

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. • 1 bit!
• black & white (B&W)!
0 & 1

9. • 8 bits!
• 256 different intensities of gray!
• the result of measuring the intensity of light

10. • 4 bits !
• Red, Green, Blue, Intensity
Microsoft Apple

11. 1. Indexed Color (palette)!
2. RRRGGGBB (8 bits)
3-3-2 bit RGB

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. File size =!
6x3 bytes (palette) !
+ 7x5x1 bytes (indices) !
! ! ! ! = 53 bytes
File size =!
! ! 7x5x3 bytes!
! ! ! ! = 105 bytes

14. • 15-bit high color!
• 16-bit high color!
65,536 colors
15 bits
16 bits

15. • 24 bits!
256 × 256 × 265 = 16,777,216 colors!
!
!
• 32 bits
24 bits

16. • color depth!
• color model!
RGB!
CMYK!
YIQ!
YUV!
HSV & HSL

17. • RGB!
RGB 565!
RGB 888!
• CMYK!
for print!
!
• NOT intuition

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. • 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. • 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. • 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. • 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. • HSV(Hue, Saturation, Value) or HSB(B, Brightness)!
• HSL(Hue, Saturation, Lightness) or HLS

24. • Image Basic Concepts!
• Image Processing!
Edge Detection!
Histogram!
Noise Removal!
Threshold!
• Lab

25. Image Processing
Processing Recognition
Computer Vision

26. • Edge!
• Laplacian!
• Sobel!
• Prewitt!
• Canny

27. • Edge!
• derivative

28. • derive from 2nd derivative

29. • 1st derivative

30. • 1st derivative

31. • Double threshold!
• computation : sobel > canny!
• result : canny > sobel

32. • probability density function (pdf)!
• cumulative distribution function (CDF)!
• equalization

33. • histogram equalization

34. • Smoothing Method!
!
!
!
• Median Method
1,1,2,2,2,2,3,3,200

35. • Before Recognition!
After edge detection!
• grayscale image!
• image size is still large!
Image Segmentation!
• binary image (black & white)!
threshold

37. Histogram!
!
histogram
equalization
Edge!
Detection!
laplacian
sobel
prewitt
Threshold!
set
threshold
Noise!
Removal!
smoothing
median
Gray level!
RGB
to
Gray

38. • Image Basic Concepts!
• Image Processing!
• Lab
38