1. Simple Image Processing Speaker : Lin Hsiu-Ting Date : 2005 / 04 / 27

2. Outline  Concept of Image Processing  Space Domain Image Processing  Frequency Domain Image Processing  Geometry Transform  Shape Processing  Color System

3. Concept of Image Processing

4. Concept  The “Image” Signals We Can See Include Special Information We Process These Signals To Get Relative Information  Integration Technology Engineering Mathematics Physical Biology Medical Science Entertainments

5. Concept  Application Digital Photo Map Natural Disaster Monitored Others…  Relative Software Photo Shop Photo Impact Others… These Aren’t Today Key Points

6. Concept  General Topics of Image Process Image Capture & Image Digitize Image Stretch & Remove Distortion Shape Process Image Features Extracted Color Image Process Image Coding & Compression

7. Concept  Image Digitized Sampling Quantization Coding  Non-Ideal Situations In Process Quantization Error Distortion Noise

8. Image with Noise  Images Usually Suffer Noise When Sampling (Like Use Scanners or Digital Cameras…)  Some Common Noise Dot Noise Uniform Noise Sinusoid Wave Noise Gaussian Noise Other  Sometimes We Can Remove Noise According Their Features

9. Image with Noise  Dot Noise  Uniform Noise

10. Image with Noise  Sinusoid Wave Noise  Gaussian Noise

11. Space Domain Image Processing

12.  Characteristic Representation Profile Histogram Statistic ( Mean & Standard Deviation )  Point Operation Binarization Inverse Contract Stretch Histogram Equalization Gamma Correction Arithmetic & Logic Operation

13. Binarization  Before Binarization ( 8-bit Gray Level )  Binarization (Threshold = 200)

14. Contract Stretch  Before Processing  After Processing Process Flow Load Image Histogram Statistic Stretch

15. Histogram Equalization  Before Processing  After Processing Process Flow Load Image Histogram Statistic Equalization

16. Arithmetic (Add & Sub)  Image #1  Image #2  Image #1 + Image #2  Image #1 - Image #2

17. Space Domain Image Processing  Range Operation Smoothing ( Low Pass Filter ) Median Filter High Pass Filter Differentiation  Mask Matrix Note ： We Can Also Use 5x5, 7x7 or Larger Matrix Process Range Operation But It Cause More Computing

18. Median Filter  Before Processing  After Processing For Every 3 x 3 Block Search C n = Median (C) Let f (x, y) = C n Note : The Method Will Have Poor Result When A Lot Of Noise Cluster

19. Frequency Domain Image Processing

20.  Fast Fourier Transform  Implement Recursion Algorithm Butterfly Algorithm  Easy To Achieve Filter High Pass / Low Pass Band Pass / Notch

21. Frequency Domain Image Processing  2D Fast Fourier Transform Do FFT For Every Row ……………...................................... Do FFT For Every Column F ( u, v ) Note : We Always Use Log Unit Present The Spectrum Distribute Instead of Linear Because Its Dynamic Range is Larger Then Screen

22. Frequency Domain Image Processing  Image  Spectrum  Image with Sin Noise  Spectrum

23. Geometry Transform

24.  Coordinates Transform Rotation Scaling Twist  Gray Level Interpolation Replicative Interpolation Bilinear Interpolation

25. Coordinates Transform  Rotation  Scaling  Twist

26. Gray Level Interpolation  When We Transform From R to R* Some Point In R* Can’t Correspond From R Rotation, Magnify Suffer This Question Ex: Magnify 123 456 789 1?2?3? ?????? 4?5?6? ?????? 7?8?9? ??????

27. Gray Level Interpolation  Replicative Interpolation Use The Nearest Point To Present Let j = Int(x+0.5), k = Int(y+0.5) => g ( x’, y’ ) = f ( j, k )  Bilinear Interpolation Use Four Neighborhood Points More Smooth Than Replicative

28. Gray Level Interpolation  Replicative Interpolation  Bilinear Interpolation

29. Shape Processing

30.  Find The Edges And Bones Binarization  Process The Edge And Bone Erosion Dilation Open / Close Remove Isolate Points  Usually Simple Logic Operation

31. Erosion & Dilation  Binarization Image  Erosion  Dilation

32. Color System

33.  The Colors We See Wave Length 380 nm ~ 780 nm Use Rods to Recognize Brightness Use Cones to Recognize Colors (Three Types For R. G. B. Colors) Usually Eyes Are More Sensitive To Brightness Than Colors This Feature is Convenient For Image Compressing

34. Color System  Common Color System R. G. B. System (Red, Green and Blue) C. M. Y. System (Cyan, Magenta and Yellow) -- A Complement of R. G. B Y. U. V System Y. I. Q System H. S. I. System

35. Conclusion  Image Processing Is Useful  Image Processing Is Interesting  Although We Needn’t Know The Details Of Techniques Because Many Powerful Software Will Handle Them…  But Knowing General Concept Is Helpful For Us

36. Reference  數位影像處理 - 連國珍 著, 儒林出版  http://www.cs.ecnu.edu.cn/teach/down /dip/Chapter02.pps  http://www.fosu.edu.cn