INFRARED TARGET EXTRACTION ALGORITHM BY USING PARTICLE SWARM OPTIM IZATION PARTICLE FILTER

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1 J. Infrared M illim. W aves Vol. 29, o. 1 February, 2010 : (2010) , 2 (1., ; 2., ) : ( PSOPF),.,,,,.,.,. : ;; ; : TP391. 4: A IFRARD TART XTRACTIO ALORITHM BY USI PARTICL SWARM OPTIM IZATIO PARTICL FILTR ZHOU Yue 1, MAO Xiao2an 2 (1. Institute of Pattern Recognition and Image Processing, Shanghai J iaotong University, Shanghai200240, China; 2. Control ngineering Institute of SAST, Shanghai200233, China) Abstract: A novel infrared target extraction algorithm based on particle swarm op tim ization particle filter ( PSOPF) was p roposed. The p roblem of infrared target extraction was analyzed and solved in the view of state estimation. In the frame2 wor of particle filter, the threshold state space on the gray2variance weighted information entropy and the gray value of each p ixel was established. Particle swarm op tim ization was introduced to construct the state transition model. The observation model based on extraction results evaluation function was constructed, which integrated gray, entropy, gradient and spatial distribution of p ixels. Finally, the weighted average of all the particles was used as target extraction threshold. The experi2 ment results p rove that the p roposed algorithm is effective and robust. Key words: particle filter; particle swarm op tim ization; target extraction; gray2variance weighted information entropy,,., [ 1, 2 ] [ 3 ] [ 4 ]. (),, [ 2 ] [ 5 ]. O tsu, [ 1, 2 ],,, [ 6, 7 ]., ( ),., [ 8, 9 ]. [ 6 ] Kennedyberhart 1995.,, : , : Rece ived da te: , rev ised da te: : ( ) ; : ( ),,,,, 2Mail: zhouyue@ sjtu. edu. cn.

2 64 29,, [ 7 ]. :,, ;,, ;,, ;,,,. 1, R T.,, R T,. S bg, S obj,s bg S obj =, S bg S obj = R T. S obj. 1. 1, R T 2 S U : = 1 K K i, (1), KR T ; U = 1 U U s i, (2), U S U, s i S U i ; i = i H i, (3), i i, H i i [ 10 ]. = 1, T = 1, T = 2 2 U S U,S U, T T,. T T, = 1, 0 < 1 < 2 { i }, 2,, K, 0 < 2 { s i }, 2, U <, U, [ 9 ],.,, 0, 0,, 2,, U { s i } U, 2,, K { i },, w 0 = 1., ( ) L ( ) : ( ) L = arg{w ( l),, w,, w ( ) }, i = 1, 2, (4) ( ) = arg{w ( l),, w,, w ( ), w ( - 1) }, L = 1, 2, (5), w i, w ( - 1) - 1 ( - 1). 1,. 1 = 0 + U 1 = 1 = ( ( - 1) - - 1) U 1 + ( ( - 1) L ) - - 1) U 2 2,, (6), U 1, U 1, U 2, i = 1, 2,,. ( 7) , ,,,.

3 1 : 65 = 1 s j, (7) j =1, i = [ 1,, 2, ] T S..,,. : ( d i, j ) = d2 1 2 e- 22, (8) R T ( i, j) ( d i, j ), d R T ; R T H R T W R T, = 10 H R T W R T. i = [ 1,, 2, ] T S :, = 1 ( d j ) j, (9) j =1, d j S j R T, j j. { }, 2,,. Sobel,. : rad = 1 g 2 1, j + g 2 2, j, (10) j =1, S, g 1, j g 2, j S. { rad j }, 2,,. i = [ 1,, 2, ] T S, O,S O. Con = 1 P j =1 j, - O. (11) :S ,,.,,. { ( l), (2),, ( ) }, { S (1), S (2),, S ( ), }, = [ ( 1), (2),, ( ), ] T, m v, : = - m v. (12) ( x) = 1 x2 2 e- 2 =( ),., { Con { }, 2,,., : V = 4, exp ( 1, = 2, = 3, = 4, =, rad Con 1,, 2,, 3,, }, 2,,, 1, 2, 3, ), (13) 4,. = {, w } :,w : = V w V. (14) = gw. (15),. 1 ( a1) ( a4)( b1) ( b4),. 3,,., ( a1) ( a4 )( a1 ), ( b1 ) ( b4)( b1). ( 13),1. = 15,1.

4 ( = 15) Fig. 1The extracting results with different thresholds ( number of particles = 15) 1 Table 1The results of the eva lua tion function V ( ) () () ( ) rad ( ) () a a a a b b b b i = 1:, = [, 1,, 2 ] T, (15) w., (5) (6) 4. (16), +1 -,, ; [ 9 ], :. : , = 011;,,, = 011,. 1.., 0 < 1 <, 2,, K { i }, 0 < 2 <, 2,, U { s i } U,, 0 = { 0, w 0 }, 2,,, w 0 = 1, = (7) = {, w } :. Pentium 2. 40Hz PC VC , 2 ( a) ( e). 2( a) ( b)( c),,2d2 ( c), 2D2 ( a) ( b). ( d) ( e), 2D2 2D2,,.,2D2O tsu,, , [ 2 ] PSO, 15, 11, 165.,15, 3, 45

5 1 : 67 23, 2D2 2D2 Fig. 2The comparison of 3 target extraction algorithm s, the original image, our algorithm, 2D2O tsu, 2D2imum entropy method from left to right,. 3,.,,, 2D2O tsu,.,

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