1. Chapter 1. Introduction

2. Goals of Image Processing “One picture is worth more than a thousand words” 1.Improvement of pictorial information for human interpretation. 2.Processing of scene data for autonomous machine perception.

3. Related Areas of Image Processing Image Processing: image  image Computer Graphics:information  image Computer Vision:image  information

4. 1.Image Analysis 2.Image Restoration 3.Image Enhancement 4.Image Compression Applications of Image Processing

5. Example of Image Restoration

6. Example of Image Enhancement

7. Steps in Digital Image Processing

8. Digital Image

9. Sampling & Quantization

10. Sampling

11. Quantization False contours

12. Storage requirement A MxN image with 2 k gray scales # of storage bits = M x N x k

13. Example Generally, transmission is accomplished in packets consisting of a start bit, a byte of information, and a stop bit. Using this approach, how many seconds would it take to transmit a 1024x1024 image with 256 gray levels at 300 baud (bits/sec)?

14. Types of Images  Analog Image  Digital Image 1.Binary Image 2.Gray-scale Image 3.Color Image 4.Multispectral Image

15. Multispectral Image

16. Electromagnetic Spectrum

17.  Vector Image  Bitmap Image RAW  no header RLE (Run-Length Encoding) PGM,PPM,PNM (Portable Gray Map) GIF (Graphics Interchange Format)  no more than 256 colors TIF (Tag Image File Format)  Scanner EPS (Encapsulated Postscript)  Printer JPEG (Joint Photographic Experts Group)  Compression ratio MPEG (Motion Picture Experts Group)  Video Image Formats

18. Comparison of Image Formats

19. Human Visual Perception

20. Machine Visual Perception

21. Perception of objects 1.The spectrum (energy) of light source. 2.The spectral reflectance of the object surface. 3.The spectral sensitivity of the sensor (eye or camera).

22. Human eye

23. How do we see an object? Light Object Eye Luminance  Lightness  Rods Chrominance  Color  Cones Human eye is more sensitive to luminance than to chrominance

24. Cones & Rods ( day & night )

25. Three kinds of Cones

26. Brightness adaptation

27. Brightness illusions: Mach band effect

28. Contrast illusions

29. Geometric illusions

30. Spatial & Temporal Resolution Spatial resolution: 4-50 cycles per degree Spatial resolution: 4-50 cycles per degree Temporal resolution: 50 cycles per second Temporal resolution: 50 cycles per second Brightness resolution: 100 gray levels Brightness resolution: 100 gray levels

31. Color Spectrum

32. Electromagnetic spectrum

33. RGB Model

35. RGB signals from a video camera

36. Color measurement: A mixture of red, green, and blue light Values between 0.0 (none) and 1.0 (lots) Color examples Red Green Blue White 1.0 1.0 1.0 Black 0.0 0.0 0.0 Yellow 1.0 1.0 0.0 Magenta 1.0 0.0 1.0 Cyan 1.0 1.0 0.0 RGB Model

37. rgb Model(Normalized RGB) r+g+b=1

38. Chromaticity Diagram

39. Typical color gamut

40. HSI Model

42. Color Complements

43. CMY Model

44. Light vs. Pigment

45. YIQ Model TV transmission  digital space  YC B C R  analog space  YIQ (NTSC)  YUV (PAL)

46. YUV & YC B C R Model

47. TV Broadcast