數位影像中熵的計算與應用

1. 數位影像中熵的計算與應用 義守大學 資訊工程學系 黃健興

2. Outline • Entropy • Definition • Entropy of images • Applications • Visual Surveillance System • Background Extraction • Conclusions

3. Concept of Entropy • Rudolf Julius Emanuel Clausius , 1864 • 化學及熱力學 • 測量在動力學方面不能做功的能量總數 • 計算一個系統中的失序現象 • 描述系統狀態的函數 • 經常用熵的參考值和變化量進行分析比較

4. Information Theory • Claude Elwood Shannon , 1948 • 運用機率論與數理統計的方法研究資訊 • 編碼學 • 密碼學與密碼分析學 • 數據傳輸 • 數據壓縮 • 檢測理論 • 估計理論 • 數據加密

5. Definition • E is the expected value, • I is the information content of X. • p denotes the probability mass function of X

6. Advantage • Whole Image • M×N Matrix • Histogram • N×1 Vector • Entropy • Single value

7. Entropy of Image • Pixel Color • Pixel Distribution • Horizontal • Vertical • Texture

8. The Statistic of gray-level

9. Position Information • Normalize the size of image • Edge Detection • Sobel • Canny • Horizontal Projection • Vertical Projection

10. Sobel Edge Detection • Sobel Filter

11. Sobel Edge Detection(cont.)

12. Sobel Edge Detection(cont.)

13. 0 240 Horizontal Projection

14. Horizontal Projection(cont.)

15. 0 320 Vertical Projection

16. Vertical Projection(cont.)

17. Local Binary Pattern • Pattern Texture • Pattern • Center Pixel gc • Surrounding Pixel gi(i=0, 1,…,p-1) • Label

18. Local Binary Pattern(cont.)

19. Definition • E is the expected value, • I is the information content of X. • p denotes the probability mass function of X

20. Applications • Visual Surveillance System • variance of video information • Background Extraction • Block for pixel

21. F 60 F 63 F 2 F 20 F 45 F 68 F 69 Visual Surveillance System

22. Visual Surveillance System

23. Gray Prediction – GM(1,1)

24. Gray Prediction – GM(1,1) (cont.) • Step 1: • Step 2: • Step 3:

25. Gray Prediction – GM(1,1) (cont.) • Step 4: • Step 5:

26. Gray Prediction – GM(1,1) (cont.) • Step 6: • Step 7:

27. Visual Surveillance System

28. Visual Surveillance System

29. Background Extraction • Non-recursive approaches • Selective update using temporal averaging • Selective update using temporal median • Selective update using non-foreground pixels • Non-parametric model • Time Interval (It-L,It-L+1,It-1) • Probability Density Function

30. Background Extraction • Recursive approaches • Kalman filter • Mixture of Gaussians (MoG) • Parametric model • Matching • Updata

31. Improved Method • Treat the n×n block as a pixel

32. Improved Method(cont.)

33. Conclusions • Reduce Memory Size • Enhanced Performance • Quantize the content of image • Judgment of the variance