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基礎影像處理
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- 2. OUTLINE 1. Image BasicConcepts! 2. Image Processing! 3. Lab
- 3. • Image BasicConcepts! Image! Color! • Image processing! • Lab
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- 5. • Single Sensor(point)! • Sensor Stripes (line)! • Sensor Array (Array)
- 6. • dpi (dotsper 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 =! 6x3bytes (palette) ! + 7x5x1 bytes (indices) ! ! ! ! ! = 53 bytes File size =! ! ! 7x5x3 bytes! ! ! ! ! = 105 bytes
- 14. • 15-bit highcolor! • 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! RGB888! • CMYK! for print! ! • NOT intuition
- 18. • YIQ! Y providethe 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 BasicConcepts! • Image Processing! Edge Detection! Histogram! Noise Removal! Threshold! • Lab
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- 26. • Edge! • Laplacian! •Sobel! • Prewitt! • Canny
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- 28. • derive from2nd derivative
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- 31. • Double threshold! •computation : sobel > canny! • result : canny > sobel
- 32. • probability densityfunction (pdf)! • cumulative distribution function (CDF)! • equalization
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- 34. • Smoothing Method! ! ! ! •Median Method 1,1,2,2,2,2,3,3,200
- 35. • Before Recognition! Afteredge detection! • grayscale image! • image size is still large! Image Segmentation! • binary image (black & white)! threshold
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- 38. • Image BasicConcepts! • Image Processing! • Lab 38