COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 1 ppsx

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 1 ppsx

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 1 ppsx

... Verification 11 2 4. Proposed Online Signature Verification Applications 11 3 4 .1 System Password Authentication 11 3 4.2 Internet E-commerce Application 11 4 5. Conclusions 11 6 References 11 6 8. Hybrid ... Fingerprint Recognition using Minutiae and Shape 11 9 Asker Bazen, Raymond Veldhuis and Sabih Gerez (The Netherlands) 1. Introduction 11 9 2. Elastic Deformations 12...

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COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 1 ppt

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 by Muhammad ... 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...

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COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 2 pot

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-case letters Lower-case letters Number of instance vectors 19 16 210 9 610 0 7245 12 10 5 23 710 Number of prototypes 10 8 18 6 558 278 723 15 77 Prototype...

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COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 3 pptx

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 3 pptx

... Classification 85 0 5 10 15 20 25 Number of character conceptsNumber of character concepts 0 2 4 6 8 10 12 14 16 1 to 10 11 to 20 21 to 30 More than 30 Number 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,” Human Movement Science, 3, pp. 19 3–...

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COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 4 doc

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 4 doc

... on Pattern 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5 C100 0000 0 00098 81 2 Table 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...

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COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 5 doc

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 5 4 3 2 1 0 Eigenvalues 10 7 10 00 800 600 400 200 0 Eigenvalues Figure ... X, Y and Z values: L ∗ = 11 6Y/Y 0  1/ 3 16 a ∗ = 500  X/X 0  1/ 3 −Y/Y 0  1/ 3  (12 .2) b ∗ = 200  Y/Y 0  1/ 3 −Z...

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COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 6 pptx

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 6 pptx

... step. 1 2 3 4 5 6 7 S1 S2 S3 S4 S5 S6 S7 50 55 60 65 70 75 80 85 90 95 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...

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COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 7 doc

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 7 doc

... j∈K f 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 2 Extractor K Classification criteria f(x) (1) f(x) (2) f(x) (k) f(x) (1) f(x) (2) f(x) (k) Classification results Input vector ... information Classification Experiments 315 Recogniti...

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COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 8 pps

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 Curves 0 010 010 010 010 010 010 010 010 . . . . . . . . . . 16 26 22 18 17 25 26 25 Figure 19 .24 ... connections. (a) (b) (c) (d) 2 2 1 4 1 6 7 3 3 5 5 11 12 8 9 10 10 11 12 7 6 24 35 11 12 13 78 9 8 13 13 4 10 Figure 19 .11 The SCR graph, adjacency graphs...

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COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 9 pps

COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 9 pps

... (c) + + + + + + W 1N W 21 gp W 21 gp W 22 gp W 2n gp θ 2 θ 2 θ N θ 1 W 11 W 12 W 1N W 12 W 12 Figure 21. 4 Geometric network structures for approximation. (a) Cybenko’s; (b) GRBF network; (c) GMLP pqr . Equation ( 21. 19) ... multivectors. Initialize  i = 0 For t = 1tot max if t = 0 b 0 = 0 1 else if t = 1 b 1 =−0 1 else b t = b t 1 −w t 1  b t 1 −b t−2 w t 1 −w t−...

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