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

資訊工程系 Outline ﻪIntroduction ﻪLiterature ﻪMethods ﻪExperiments ﻪConclusion 2

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

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

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

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

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

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

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

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

資訊工程系 Method - Iterative scheme 11

資訊工程系 Methods(2/13) 12

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

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

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

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

資訊工程系 Method -Variant of PSO 17

資訊工程系 Methods(7/13) 18

資訊工程系 Methods(8/13) 19

資訊工程系 Methods(9/13) 20

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

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

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

資訊工程系 Methods(12/13) 24

資訊工程系 Methods(13/13) 25

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

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

資訊工程系 Experiments(3/11) 28

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

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

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

資訊工程系 Experiments(7/11) 32 ImagescThresholds (Proposed method) Proposed Method Uniformity values Gaussian- smoothing method Symmetry- duality GA- method Lena295, ,151, ,137,163, ,114,143,167, Pepper280, ,122, ,120,148, ,98,136,170, Table.3 Compared with those of Gaussian, Symmetry-duality and GA-based methods

資訊工程系 Experiments(8/11) 33 Imagesc Uniformity values Proposed method Basic PSO Threshold values Proposed method Basic PSO House ,178122, ,143, ,155, ,142,179,20188,153,176, ,131,179,206,21582,142,174,213,217 Elaine ,160122, ,166,202126,156, ,148,176,192112,192,166, ,122,149,174,21090,127,180,184,213 Table.4 Results of the proposed method for House and Elaine images

資訊工程系 Experiments(9/11) 34 ImagescUniformity values Threshold values Lena ,111,138,163,185 Pepper ,81,117,152,161,181 House ,143,181 Elaine ,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

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.

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.

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

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

資訊工程系 Thank you for listening 39