1. Digital Image Fundamentals Human Vision Lights and Electromagnetic spectrum Image Sensing & Acquisition Sampling & Quantization Basic Relationships b/w Pixels

2. Digital Image Processing2 Important dates  9/29: Project grouping (2~3 members/group)  10/6: First image processing GUI due!! OpenCV ImageMagick ImageJ Ximage or ImageX

3. Digital Image Processing3 A Cross Section of the Human Eye Iris – 虹膜 Lens – 水晶體 Cornea – 角膜 Sclera – 鞏膜 Choroid – 脈絡膜 Retina – 視網膜 Fovea – 視乳頭 Ciliary body – 睫狀體

4. Digital Image Processing4 Human Vision Rods: 10 8  Shape/form perception  Large dynamic range  Limited contrast  Scotopic (dim-light) vision Cones 5 X 10 6  3-channel color perception  Photopic (bright-light) vision

5. Digital Image Processing5 Distribution of Rods and Cones in the Retina

6. Digital Image Processing6 Image Formation in the Eye

7. Digital Image Processing7 Range of Subjective Brightness Visual system cannot operate over full range of subjective brightness simultaneous Via brightness adaptation

8. Digital Image Processing8 Brightness Discrimination Weber ratio / intensity ∆I c – increment of illumination discriminable 50% of the time I Small ∆I c /I => good discrimination; otherwise, poor.

9. Digital Image Processing9 Perceived Brightness Two phenomena Undershoot or overshoot around the boundaries; Mach band pattern Simultaneous contrast

10. Digital Image Processing10 Optical Illusions

11. Digital Image Processing11 Electromagnetic Spectrum

12. Digital Image Processing12 Image Sensing Single sensor Sensor strip Sensor array

13. Digital Image Processing13 A Simple Image Model i(x,y) – illumination (from light source) r(x,y) – reflectance of illuminated surface (reflectivity) Lambertian surface  Looks the same in all directions Specular (mirror-like) surface  Incidence angle = reflectance angle

14. Digital Image Processing14 A Simple Image Model (continued) f(x,y) = i(x,y) X r(x,y) >= 0 r(x,y)  0.93 white snow  0.01 black velvet i(x,y)  9000 foot-candle Sun  0.01 foot-candle full moon

15. Digital Image Processing15 Sampling & Quantization

16. Digital Image Processing16 A Digital Image of MXN Array

17. Digital Image Processing17 A Digital Image (continued) Image Sampling – Spatial-coordinate digitization Gray-level Quantization – amplitude digitization N = size of image = (number of columns) X (number of rows) G (number of gray levels) = 2 k Disk storage needed = N * ceiling(k/8)

18. Digital Image Processing18 Storage Bits for N and k

19. Digital Image Processing19 Spatial Resolution

20. Digital Image Processing20 Amplitude Quantization

21. Digital Image Processing21 Level of Detail (LOD) Low level of detail           High level of detail

22. Digital Image Processing22 Isopreference

23. Digital Image Processing23 Scaling and Interpolation

24. Digital Image Processing24 Basic Image Topology Neighbors of a Pixel  4-neighbor and 8-neighbor  4-adjacent and 8-adjacent Connectivity  4-connectivity  8-connectivity  M-connectivity (mixed connectivity)

25. Digital Image Processing25 M-Connectivity

26. Digital Image Processing26 Further Pixel Relationships Connected Component Labeling Relations, Equivalence, and Transitive Closure Distance Measures Arithmetic/Logic Operations Mask Operations

27. Digital Image Processing27 Logic Mask Operations

28. Digital Image Processing28 Weighted Mask Operation

29. Digital Image Processing29 Utilizing ALU Parallel Processing