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... Verification 11 24. Proposed Online Signature Verification Applications 11 34 .1 System Password Authentication 11 34.2 Internet E-commerce Application 11 45. Conclusions 11 6References 11 68. Hybrid ... Fingerprint Recognition using Minutiae and Shape 11 9Asker Bazen, Raymond Veldhuis and Sabih Gerez (The Netherlands) 1. Introduction 11 92. Elastic Deformations 12 03. Elastic Minutiae Matching 12 23 .1 ... underscores (see Figure 1. 1).Table 1. 1 Different forms of Arabic characters. COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS ...

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 1 ppt

... Foundation, second edition, Prentice Hall, 19 99. COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS Edited byMuhammad ... pp. 10 37 10 44, 19 90.[28] Duin, R. P. W. “Superlearning and Neural Network Magic,” Pattern Recognition Letters, 15 , pp. 215 – 217 , 19 94.[29] Jain, A. K. and Mao, J. “Neural Networks and pattern recognition, ” ... ISCAS’94, pp. 415 – 418 , 19 94. [17 ] Boyce, J. F. and Hossack, W. J. “Moment Invariants for pattern recognition, ” Pattern Recognition Letters, 1, pp. 4 51 456, 19 83. [18 ] El-Dabi, S. S., Ramsis, R. and Aladin...

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 2 pot

... Engineering, 34, pp. 15 9 16 5, 19 95. [16 ] Shi, Z. and Govindaraju, V. “Segmentation and Recognition of Connected Handwritten Numeral Strings,”Pattern Recognition, 30, pp. 15 01 15 04, 19 97. [17 ] Strathy, ... Upper-caselettersLower-caselettersNumber ofinstance vectors 19 16 210 9 610 0 7245 12 10 5 23 710 Number of prototypes 10 8 18 6 558 278 723 15 77Prototypes per label ratio 10 872 21 5278278607Previous Work 55Figure ... Recognition, 28 (1) , pp. 67–80, 19 95. [11 ] Lu, Y. and Shridhar, M. “Character segmentation in handwritten words – an overview,” Pattern Recognition, 29 (1) , pp. 77–96, 19 96. [12 ] Shridhar, M. and...

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 3 pptx

... Classification 8505 10 15 2025Number of character conceptsNumber of character concepts02468 10 12 14 16 1 to 10 11 to 20 21 to 30 More than30Number of prototypes 1 to 10 11 to 20 21 to 30 More ... (6),pp. 866–8 71, 19 81. [20] Teulings, H. L. and Maarse, F. L. “Digital recording and processing on handwritting movements,” HumanMovement Science, 3, pp. 19 3– 217 , 19 84.[ 21] Kerrick, D. D. and Bovik, ... Magazine, pp. 4 16 , 19 86. [17 ] Yang, L. et al . “Application of Hidden Markov Model for Signature Verification,” Pattern Recognition, 28(2),pp. 16 1 17 0, 19 95. [18 ] Kashi, R. S. Hu, J. and Nelson,...

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 4 doc

... onPattern Analysis and Machine Intelligence, 15 , pp. 11 48 11 61, 19 93. [10 ] Bertillon, A. “La couleur de 1 iris,” Rev. Sci., 36(3), pp. 65–73, 18 85. [11 ] Flom, L. and Safir, A. Iris recognition system, ... 009750 25C100 0000 0 00098 81 2Table 10 .4 Percentage recognition for RBFNN with 30 dB SNR.C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C 11 C1 99 0 0 0 0 0 0 0 0 0 1 C2 0 98700 0 0000 01 3C3 0 0 10 0 0 0 0 ... and complexity in comparison with the MFNN. A SISO 16 4 Iris Recognition Using Neural NetworksTable 10 .3 Percentage recognition for RBFNN with 10 dB SNR.C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C 11 C1...

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 5 doc

... vectors. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 No. of eigenvalues of between–class covariance 12 345678 910 111 213 1 415 No. of eigenvalues of within–class covariance 5432 1 0Eigenvalues 10 7 10 008006004002000EigenvaluesFigure ... X, Y and Z values:L∗= 11 6Y/Y0 1/ 3 16 a∗= 500X/X0 1/ 3−Y/Y0 1/ 3 (12 .2)b∗= 200Y/Y0 1/ 3−Z/Z0 1/ 3where X/X0> 0 01 Y/Y0> 0 01 and Z/Z0> 0 01. ... ibio 1  1 ≤ i ≤ N (12 .29)2. Induction:t +1 j=Ni =1 tiaijbjot +1  1 ≤ t ≤T 1 1 ≤ j ≤N (12 .30)3. Termination:PO=Ni =1 Ti (12 . 31) 208 Recognition...

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 6 pptx

... step. 1 234567S1S2S3S4S5S6S750556065707580859095 10 0 10 5 11 0 11 5 12 0 12 5 13 0 13 5 14 0 14 5 15 0 15 5 16 0 16 5 17 0 17 5 18 0 18 5 19 0 12 5 12 3 11 3 12 7 11 2 12 5 13 5 17 5 17 9 12 8 12 7 12 7 12 1 13 2 18 5 17 9 17 6 12 2 11 7 11 1 11 4 17 4 15 9 18 2 17 1 15 2 12 2 11 3 18 3 17 8 18 5 17 6 17 3 13 9 12 3 17 0 19 2 18 6 18 1 17 7 16 9 11 9 18 7 18 6 18 0 18 1 18 6 17 5 18 3Figure 15 .16 3D projection of ... step. 1 234567S1S2S3S4S5S6S750556065707580859095 10 0 10 5 11 0 11 5 12 0 12 5 13 0 13 5 14 0 14 5 15 0 15 5 16 0 16 5 17 0 17 5 18 0 18 5 19 0 12 5 12 3 11 3 12 7 11 2 12 5 13 5 17 5 17 9 12 8 12 7 12 7 12 1 13 2 18 5 17 9 17 6 12 2 11 7 11 1 11 4 17 4 15 9 18 2 17 1 15 2 12 2 11 3 18 3 17 8 18 5 17 6 17 3 13 9 12 3 17 0 19 2 18 6 18 1 17 7 16 9 11 9 18 7 18 6 18 0 18 1 18 6 17 5 18 3Figure ... for this purpose, and bestof all are the Prewitt and the Kirsch kernels. Two templates out of the set of eight are:0 1 1 1 1 −2 1 1 1 1 45 1 1 1 1 −2 1 1 −2 1 The Kirsch edge detector...

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 7 doc

... j∈Kfix (16 .37)Class 1 +1 +1 +1 +1 All j ≠ 1 1 –1Extractor 1 Class 1 +1 All j ≠ 1 Extractor 1 Class 1 +1 All j ≠ 1 1 1 1 1 1 1 1 Extractor 1 Extractor 1 Extractor 2Extractor KClassificationcriteriaf(x) (1) f(x)(2)f(x)(k)f(x) (1) f(x)(2)f(x)(k)ClassificationresultsInputvector ... informationClassification Experiments 315 Recognition rate (%)2530354045505560246 8 10 12 14 16 18 20 22(a)Dimension Recognition rate (%)504045253035246 8 10 12 14 16 18 20 22(b)PCAMCE ... antennas ina B-scan line, and iobeing the discretized lateral position (y-coordinate) of the object. SubstitutingEquations (17 .11 ), (17 .12 ) and (17 .13 ) into Equation (17 .10 ), we come to the following:j2=...

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 8 pps

... Pattern Recognition, 30 (10 ),pp. 16 51 16 60, 19 97.384 Holo-Extraction and Recognition of Scanned Curves0 010 010 010 010 010 010 010 010 . . . . . . . . . . 16 26 22 18 17 25 26 25Figure 19 .24 ... connections.(a) (b) (c) (d)22 1 4 1 673355 11 12 89 10 10 11 12 762435 11 12 13 7898 13 13 4 10 Figure 19 .11 The SCR graph, adjacency graphs of SCRs, and networks of SCRs for a view.point ... York, 19 95. [11 ] Bunke, H. and Buhler, U. Applications of Approximate String Matching to 2D Shape Recognition, ” Pattern Recognition, 26, pp. 17 97 18 12, 19 93. [12 ] Cox, I. J., Higorani, S. L. and...

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 9 pps

... (c)++++++W1NW 21 gpW 21 gpW22gpW2ngpθ2θ2θNθ 1 W 11 W 12 W1NW 12 W 12 Figure 21. 4 Geometric network structures for approximation. (a) Cybenko’s; (b) GRBF network;(c) GMLPpqr.Equation ( 21. 19) ... multivectors.Initialize i= 0For t = 1totmaxif t = 0b0= 0 1 elseif t = 1 b 1 =−0 1 elsebt= bt 1 −wt 1 bt 1 −bt−2wt 1 −wt−2for i = 1toN% for multivector and label xi= xmi ... MIT Press, Cambridge, 19 86. [11 ] Poggio, T. and Girosi, F. “Networks for approximation and learning,” IEEE Proceedings, 78(9),pp. 14 81 14 97, 19 90. [12 ] Georgiou, G. M. and Koutsougeras, C. “Complex...

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