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Chapter 1 - Introduction

Chapter 1 - Introduction. 1.1. Motivation 1.2. Why is Computer Vision Difficult? 1.3. Image Representation and Image Analysis 1.4. Summary. 1.1. Motivation. An image is worth thousands of words. Objectives of image processing: 1. Human perception

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Chapter 1 - Introduction

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  1. Chapter 1 - Introduction 1.1. Motivation 1.2. Why is Computer Vision Difficult? 1.3. Image Representation and Image Analysis 1.4. Summary

  2. 1.1. Motivation An image is worth thousands of words Objectives of image processing: 1. Human perception 2. Machine interpretation

  3. Human Perception Before After 2

  4. Before After

  5. Before After

  6. Before

  7. After

  8. For you, ...not much can be done!

  9. Machine Interpretation Optical Character Recognition (OCR) A 1 0 Z

  10. Object Recognition

  11. Object models

  12. Model-Based Object Recognition

  13. License Number Identification Input image Location Recognition GG4025 14

  14. Image Understanding • How many people, adults, and children • are there in the picture? (ii)What are their spatial relationships? (iii) Who are they? (iv) Where are they? (v) What are they doing? 15

  15. Machine interpretation of images requires diverse methods of Mathematical Engineering Biological disciplines Psycho-physiological Intelligent Scientific

  16. Image Analysis Low-level processing: e.g., noise removal, deblurring, and contrast enhancement Mid-level processing: e.g., edge, region, corner, and texture detections High-level processing: e.g., object, function, relationship, event, and activity recognitions

  17. 1.2. Why is Computer Vision Difficult? (1) Loss of information in 3D  2D

  18. (2) Noise (3) Too much data (4) Local window vs. global view

  19. (5) Sequential vs. parallel processing

  20. 25 line segments 21

  21. Parallel processing 26 line segments 22

  22. Sequential processing 23

  23. 1.3. Image Representation SceneG(x,y,z): a 3-D continuous function Image F(x,y): a 2-D continuous function Discrete imageD(r,c): a 2-D discrete function Digital imageI(r,c): an array of discrete values M Origin M × N : Image size ○ N

  24. Dynamic range (or color depth): number of bits for a single pixel (a) 1 - bit: black and white (binary image) (b) 8 - bit: gray-scale (gray scale image) (c) 24 - bit: true color (color image)

  25. An image file is a binary file, which can be shown in hexadecimal dump. • Physically,

  26. Types of file formats: BMP : Microsoft Bitmap formal JPEG : Joint Photographics Experts Group TIFF : Tagged Image File Format GIF : Graphics Interchange Format PNG : Portable Network Graphics HDF : Hierarchical Data Format PCX : PC Paintbrush XWD : X Window Dump ICO : ICOns CUR : CURsor

  27. An image file contains (a) Header: Characteristics of image Image size Color map Compression method (b) Image data: Pixel values, Index values

  28. BMP Format

  29. Example:

  30. Reading header information

  31. C/C++ Program http://www.cs.ucsd.edu/classes/sp03/cse190-b/hw1/

  32. GIF Format

  33. Example:

  34. TIFF Format

  35. References http://www.cs.umass.edu/~verts/cs32/endian.html http://www.phy.ntnu.edu.tw/moodle/mod/resource/view.php?id=136 http://en.wikipedia.org/wiki/JPEG

  36. Homework 1: • Develop a program to read in and write out a color image. You can download the program “bmp_io.rar” from http://www.cs.ucsd.edu/classes/sp03/cse190-b/hw1/ (2) Transform a color image C(R,G,B) into a gray scale image I by I = (R+G+B)/3.

  37. Summary • Human vision is natural and seems easy; computer mimicry of this is difficult. • Processing moves from digital manipulation, pre-processing, segmentation, recognition to understanding. They may be simultaneous and co-operative. • An understanding of the notions of heuristics, a priori knowledge, syntax, and semantics is necessary.

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