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.)

13. Horizontal Projection 0 240

14. Horizontal Projection(cont.)

15. Vertical Projection 0320

16. Vertical Projection(cont.)

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

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

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

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 (I t-L,I t-L+1,I t-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