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Introduction to Image Processing: Goals, Applications, and Perception

This chapter delves into the fundamental aspects of image processing, highlighting its significance in enhancing pictorial information for human interpretation and enabling autonomous machine perception. The discussion includes related areas such as computer graphics and computer vision, alongside applications like image analysis, restoration, enhancement, and compression. Furthermore, it examines different types of images, image formats, and the nuances of human and machine visual perception. Understanding these components is essential for anyone entering the field of image processing.

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Introduction to Image Processing: Goals, Applications, and Perception

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  1. Chapter 1. Introduction

  2. Goals of Image Processing • “One picture is worth more than a thousand words” • Improvement of pictorial information for human interpretation. • 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. Applications of Image Processing • Image Analysis • Image Restoration • Image Enhancement • Image Compression

  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 2k 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 • Binary Image • Gray-scale Image • Color Image • Multispectral Image

  15. Multispectral Image

  16. Electromagnetic Spectrum

  17. Image Formats • 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

  18. Comparison of Image Formats

  19. Human Visual Perception

  20. Machine Visual Perception

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

  22. Human eye

  23. How do we see an object? Light Eye Object • 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 • Temporal resolution: 50 cycles per second • Brightness resolution: 100 gray levels

  31. Color Spectrum

  32. Electromagnetic spectrum

  33. RGB Model

  34. RGB Model

  35. RGB signals from a video camera

  36. RGB Model • 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

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

  38. Chromaticity Diagram

  39. Typical color gamut

  40. HSI Model

  41. HSI Model

  42. Color Complements

  43. CMY Model

  44. Light vs. Pigment

  45. YIQ Model • TV transmission  digital space  YCBCR •  analog space  YIQ (NTSC) •  YUV (PAL)

  46. YUV & YCBCR Model

  47. TV Broadcast

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