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1、2010 4 Journal on Communications April 2010 31 4 V ol.31No.4 ( 150001 Contourlet Contourlet Contourlet Contourlet TP391.43 A 1000-436X(201004-0128-06 New method of banknote feature extractionGAI Shan, LIU Peng, LIU Jia-feng, TANG Xiang-long(School of Computer Science and Technology, Harbin Institute
2、 of Technology, Harbin 150001, ChinaAbstract: Combined with the merits of Contourlet transform and fuzzy logic, a new method of banknote image feature extraction was proposed. Contourlet coefficients were extracted in different scales and directions by using Contourlet transform on the banknote imag
3、e. The extracted coefficients were considered as linguistic variables. Then it calculates the firing strength on the fuzzy feature plane and normalizes them as feature vector. Coarse classification was conducted by banknote geometric features. Then the neural network was constructed for the recognit
4、ion of banknote and the scheme of reject class was introduced. The experimental results show that this method achieves a high recognition rate and fast rec-ognition speed. The proposed method was applied in the resource constrained of embedded system.Key words: Contourlet transform; fuzzy logic; lin
5、guistic variable; neural network; banknote recognition1- Q 2009-10-16 2010-02-05-(60702032-(2009RFQXG208-(QC2009C06-(2009B010800069Foundation Items: The National Natural Science Foundation of China(60702032; Harbin Special Funds for Technological Innova-tion Research(2009RFQXG208; The Natural Scienc
6、e Foundation of Heilongjiang(QC2009C06; The Science and Technology Plan Project of Guangdong Province(2009B010800069 4 g 129g8 / 1 2 Haar 3 4,51 Takeda 1993 (slab value L 2003 Viola Haar Haar Boosting 6 K-L Contourlet 7,8 L L üü Haar E Contourlet (CFFE, Contourlet-fuzzy feature extraction
7、BP 650 2 2.1 Contourlet 2 Do M N2002 Contourlet (contour segment Contourlet E Contourlet (FDFB 2Burt 91983 (LP, Laplacian pyramid EE E E (DFB, directional filter banks 10E LP DFB 2.2 11 12 , g 130g 31 39 3 Contourlet 3.1 CFFE Contourlet Contourlet ( I t (1 0,1, ( min( max(min(I t I t f t I t I t = (
8、1 (1 ( f t J , K Contourlet E( j cA t E , , ( j k m cD t (1, 2, , 2; 1, 2, , ; 1, 2, , 2 J Kj k K m =L L L ( ( j jcA t cA t = ( f t E , , , , ( =( j k m j k mcD t cD t ( f t E ( cA t ( cD t A L D L CFFE A L D L T T ,1,21,2, , , , , , , A A A A,nA D D, D D,nD V V V =V V V =L L T T (2nA nD A T D T , (
9、1, 2, , A k V k nA =L , (1, 2, , D l V l nD =L , A D L L A T D T ,1,2, ,1,2, TT (, (, , (, (, , (AA A A nADD D D nDT V V V T V V V cA t =cA t cA t cA t cD t =cD t cD t cD t µµµµµµµµL L (3(1, , A,k V k nA =L µ ,l (1, , D V l nD =L µA L D L k l ( cA t
10、( cD t A L D L (A D A A T D D T cA t cA t cD t cD t =S T µS T µ (4 (A cA t S (D cD t S , A D L L (,A t cA t =S C S(D cD t S T ( ( (t cA t cD t =C (A cA t S (D cD t S (,(i, j A,i D, j i, j i, j t cA t cD t t =R S C S S V µC(5(i, j t µC (, , , , i j A i D j v v =V i, j R , i j R (,
11、 , acc , , (A i D j i j i j V V ttC t cA t cD t =× (6 (6acc, i j : A L , A i V D L , D j V , i j R (6 ,1,1,1,2, , acc1,1acc 1,2acc , ( ( ( (A D A D A nA D nD V V t V V t nA nD V V t cA t cD t cA t cD t cA t cD t × × = × M M (7 nR nA nD =× (8 acc 1,1acc 1,2acc , nA nD = M (8
12、4 g 131g3.2CFFE CFFE Contourlet 4 24 000 2005 200 CIS A/DFPGA(Xilinx XC3S200 DSP(TI TMS320C6713BFlash SDRAM 54 FT FB BT BB 20 1 200300 900 1 1 /% 50 10.8 5.8 45.54.18 2 4 4 4 8 1 (a (b (c 1 84.2CFFE CFFE Contourlet 9-7 LP 9-7LP 3 DFB PKV A ( 1, 2, 3Contourlet E 3 2E 2 4E 1 8E E 14E 2 3 Contourlet 3E
13、 E 3 Contourlet E (a (b 3 Contourlet 2 Contourlet g 132g 31 3 ContourletCFFE Contourlet EE , A D L L , c c A D , A D T T CFFE , A D T T , A D n n 8 5 A D n n ×=40 4 4 000 CFFE 85.6%91% 98.8% 20 99.3%99.1% 4 CFFEBP BP 13 Haar 13( 14 CFFE 2 14 A K 1 D K 1 2 CFFE 2 E 2.0GHz Intel Pentium 4 CPU13 8
14、0ms 14 65ms 25ms 2 /% /% /% 13+BP 20 000 17 800 2 200 89.00 0.35 2.45 14+BP20 00018 890 1 11094.450.27 1.38 CFFE+BP 20 000 19 98515 99.920.20 0.525 Contourlet 4 Processing, 1992,40(4: 882-893. g133g Contourlet CFFE 2005 , 1 KATO N, OMACHI S. A handwriting character recognition system using direction
15、al element featureJ. IEEE Trans on Pattern Analysis and Machine Intelligence, 1999, 21(3: 258-262. 2 TAKEDA F, OMATU S. Bank note recognition system using neural network with random masksA. Proceedings of the World Congress an Neural NetworksC. USA,1993.241-244. 3 VIOLA P, JONES M, SNOW D. Detecting
16、 pedestrians using patterns of motion and appearanceA. Proceedings of the Ninth IEEE International Conference on Computer VisionC. Nice, France, 2003. 1324-1331. 4 TAKEDA F, OMATU S. High speed paper currency recognition by neural networksJ. IEEE Trans on Neural Network, 1995, 6(1: 73-77. 5 TAKEDA F
17、, OMATU S. A neuro-money recognition using optimized masks by CAM. Germany: Advance in Fuzzy Logic Neural Networks and Genetic Algorithms, 1995. 190-201. 6 , , . J. , 2003, 40(7: 159-163. LIU J F, LIU S B, TANG X L. An algorithm of real-time paper currency recognitionJ. Journal of Computer Research
18、and Development, 2003, 40(7: 159-163. 7 DO M N, VETTERLI M. Contourlets: a directional multiresolution image representationA. Proceedings of IEEE International Conference on Image ProcessingC. Rochester , NY, 2002. 357-360. 8 DO M N, VETTERLI M. The contourlet transform: an efficient directional mul
19、tiresolution image representationJ. IEEE Trans on Image Processing, 2005, 14(12: 2091-2106. 9 BURT P J, ADELSON E H. The laplacian pyramid as a compact image codeJ. IEEE Trans on Communications, 1983, 31(4: 532-540. 10 BAMBERGER R H, SMITH M J T. A filter bank for the directional decomposition of images theory and designJ.IEEE Trans on Signal 11 CHANDRASHEKHAR M, GANGULI R. Uncertainty handling in structural damage detection using fuzzy l
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