1. 資訊工程系智慧型系統實驗室 iLab 南台科技大學 1 A new social and momentum component adaptive PSO algorithm for image segmentation Expert Systems with Applications 38 (2011) 4998–5004 Akhilesh Chander, Amitava Chatterjee, Patrick Siarry, Reporter : Yu Chih Lin

2. 資訊工程系 Outline ﻪIntroduction ﻪLiterature ﻪMethods ﻪExperiments ﻪConclusion 2

3. 資訊工程系 Introduction(1/4) ﻪImage segmentation is useful in separating Background Discriminating objects (gray-levels) 3

4. 資訊工程系 Introduction(2/4) 4 ﻪUsually image segmentation can be classified as Bi-level thresholding Multilevel thresholding

5. 資訊工程系 Introduction(3/4) 5 ﻪProposed an iterative procedure Determinies the number of thresholds Positions of these thresholds

6. 資訊工程系 Introduction(4/4) 6 ﻪProposed a variant of PSO based stochatic algorithm Utilized for gray image segmentation purpose Used thresholds as initial values

7. 資訊工程系 Literature(1/3) 7 ﻪThresholding techniques can be classified into two types Optimal thresholding methods Property based thresholding methods

8. 資訊工程系 Literature(2/3) 8 ﻪBi-level thresholding has a problem Only one gray value to be found Get more and more complex by employing Multilevel thresholoding

9. 資訊工程系 Literature(3/3) 9 ﻪAll of these methods have a common problem Conputational complexity rises exponentially ﻪUse bi-level Otsu thresholding method Multilevel thresholding with less computational complexity

10. 資訊工程系 Methods(1/13) 10 ﻪProposed three methods Iterative scheme Variant of PSO Entropy criterion based fitness measure

11. 資訊工程系 Method - Iterative scheme 11

12. 資訊工程系 Methods(2/13) 12

13. 資訊工程系 Methods(3/13) ﻪIterative scheme 13

14. 資訊工程系 Methods(4/13) ﻪSelf-iterative scheme 14

15. 資訊工程系 Methods(5/13) 15 Fig1. Time complexities of the iterative scheme

16. 資訊工程系 Methods(6/13) 16 Fig2. Uniformity values of the proposed iterative scheme

17. 資訊工程系 Method -Variant of PSO 17

18. 資訊工程系 Methods(7/13) 18

19. 資訊工程系 Methods(8/13) 19

20. 資訊工程系 Methods(9/13) 20

21. 資訊工程系 Methods(10/13) ﻪThe thresholds are utilized as initial thresholds Randomly distributed around the initial thresholds Find the optimal solution more efficiently 21

22. 資訊工程系 Method -Entropy criterion based fitness measure 22

23. 資訊工程系 Methods(11/13) ﻪUse Otsu’s multi-threshold entropy measure ﻪUse as objective function in PSO ﻪGray levels of a image range over [ 0, L-1 ] ﻪh(i) denote the occurrence of gray-level i 23

24. 資訊工程系 Methods(12/13) 24

25. 資訊工程系 Methods(13/13) 25

26. 資訊工程系 Experiments(1/11) ﻪProposed scheme was compared with GA-learning-Otsu algorithm Gaussian-smoothing method Symmetry-duality method ﻪUse two image, Lena and Pepper (512*512) 26

27. 資訊工程系 Experiments(2/11) ﻪCompared with the results of basic PSO employing linearly decreasing inertia weight. ﻪTwo additional images, House and Elaine 27

28. 資訊工程系 Experiments(3/11) 28

29. 資訊工程系 Experiments(4/11) 29 IterationscNo. of particlesLena image Uniformity values House imageElaine imagePepper image 1003100.96790.97910.97100.9743 150.97010.97930.97270.9750 200.97040.97940.97330.9750 250.97210.97930.97440.9759 300.97050.97980.97440.9756 Performance of PSO for varied number of particles Table.1 Uniformity values for c=3 and varying iterations for four benchmark images

30. 資訊工程系 Experiments(5/11) 30 No. of particlescIterationsLena image Uniformity values House imageElaine imagePepper image 253500.96120.97930.97460.9759 750.97130.97980.97410.9757 1000.97320.97950.97520.9756 1250.97250.97970.97490.9757 1500.97140.97930.97380.9760 Table.2 Uniformity values for c=3 and varying number of particles, four benchmark images

31. 資訊工程系 Experiments(6/11) 31 Fig.3 (a) uniformity vs. number of particles (b) uniformity vs. number of iterations.

32. 資訊工程系 Experiments(7/11) 32 ImagescThresholds (Proposed method) Proposed Method Uniformity values Gaussian- smoothing method Symmetry- duality GA- method Lena295,1540.97300.77820.81740.8885 374,151,1820.95030.87520.84760.9175 486,137,163,1960.97610.91430.92230.9333 584,114,143,167,1950.98280.90620.92770.9385 Pepper280,1460.96500.84850.84950.8741 362,122,1670.97480.87130.87020.8983 466,120,148,1820.97380.83850.83710.9072 554,98,136,170,1980.97380.88020.87710.9083 Table.3 Compared with those of Gaussian, Symmetry-duality and GA-based methods

33. 資訊工程系 Experiments(8/11) 33 Imagesc Uniformity values Proposed method Basic PSO Threshold values Proposed method Basic PSO House20.97650.9738104,178122,178 30.97980.973491,143,1185103,155,173 40.97860.971293,142,179,20188,153,176,208 50.97530.972581,131,179,206,21582,142,174,213,217 Elaine20.97730.9750107,160122,195 30.97410.9679114,166,202126,156,216 40.97500.9737103,148,176,192112,192,166,217 50.98230.974084,122,149,174,21090,127,180,184,213 Table.4 Results of the proposed method for House and Elaine images

34. 資訊工程系 Experiments(9/11) 34 ImagescUniformity values Threshold values Lena5 0.984569,111,138,163,185 Pepper6 0.980040,81,117,152,161,181 House3 0.980185,143,181 Elaine6 0.983988,114,13,148,181,213 Table.5 Results of the proposed PSO method employing self-iterative scheme to find number and values of initial thresholds

35. 資訊工程系 Experiments(10/11) 35 Fig.4 Gray images and the corresponding thresholded images of Lena (c = 5 thresholds) and Pepper (c = 6 thresholds) using the proposed method.

36. 資訊工程系 Experiments(11/11) 36 Fig.5 Gray images and the corresponding thresholded images of Elaine (c = 6 thresholds) and House (c = 3 thresholds) using the proposed method.

37. 資訊工程系 Conclusion(1/2) 37 ﻪEmploying a new proposed variant of PSO For an optimal multilevel thresholding algorithm ﻪProposes an iterative scheme to obtain initial thresholds

38. 資訊工程系 Conclusion(2/2) ﻪ Useful for practical situations because the computational complexity grows linearly with the number of thresholds ﻪProposed PSO algorithm makes a new contribution 38

39. 資訊工程系 Thank you for listening 39