1. 1-1 Chapter 1: Introduction 1.1. Images An image is worth thousands of words

2. 1-2 Human Eyeball Camera

3. 1-3 1.2. Digital Images Digital image: content of image array Pixel: picture element Gray level: pixel value (0 – 255) Camera Sensor array Image array

4. 1-4 Imaging Model

5. 1-5 Scene: a 3-D function, g(x,y,z) Image: a 2-D function, f(x,y) Origin ○

6. 1-6 Spatial Resolution

7. 1-7 Grayscale resolution (Quantization) False contours

8. 1-8 ○ Two major applications of image processing (A) Human perception (B) Machine interpretation (A) Human perception Image sharpening

9. 1-9 Noise removal Deblurring

10. 1-10 (B) Machine interpretation Image segmentation Edge detection Line drawing

11. 1-11 ○ Three levels of image processing Low-level processing – e.g., Noise removal (smoothing) Contrast enhancement Mid-level processing – e.g., Edge detection Image segmentation High-level processing – e.g., Image understanding Scene interpretation

12. 1-12 Intensity (grayscale) image ○ Types of images Binary image

13. 1-13 2-13 Color image Indexed (or palette) color image

14. 1-14 X-ray image

15. 1-15 X-ray transmission computerized tomography (CT) image

16. 1-16 Gamma-ray images

17. 1-17 Ultrasound images

18. 1-18 Ultraviolet images

19. 1-19 Radio images

20. 1-20 Multispectral images

21. 1-21 Range images

22. 1-22 Moire images

23. 1-23 Structure light images

24. 1-24

25. 1-25 Simultaneous contrast Optical illusion ○ Image Perception

26. 1-26 Overshoot and Undershoot e.g., Mack band pattern