COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 8 pps

... (1 18, 41) (41, 80 ) (80 , 38) ( 38, 56) (40, 41) (41, 80 ) (80 , 38) ( 38, 56) (106, 41) (41, 80 ) (80 , 38) ( 38, 56) (94, 41) (41, 80 ) (80 , 38) ( 38, 56) (116, 41) (41, 80 ) (80 , 38) ( 38, 56) 8. 2 Collinearity ... matches 388 Holo-Extraction and Recognition of Scanned Curves [19] Preiss, K. “Constructing the solid representation from engineering projections,” Computers and...

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... Conference on Pattern Recognition, Vienna, Austria, III (Robotics and Applications) , pp. 80 8 81 1, 1996. [12] Lin, X., Shimotsuji, S., Minoh, M. and Sakai, T. “Efficient diagram understanding with characteristic ... junction. Figure 18. 8(a) and 18. 8(b), respectively. In each extraction, there are enough line groups to guide a hypothesis for 3D object recognition. Table 18. 2 pr...

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... 177 3. Face Recognition Algorithms 1 78 3.1 Template-based Face Recognition 180 3.2 Appearance-based Face Recognition 180 3.3 Model-based Face Recognition 181 4. Linear Subspace Techniques 183 4.1 ... horizontal and vertical projections of the test document image. COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS Edited by Muhammad Sarfraz King Fa...

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... geometric algebra multidimensional neural networks andthat some of them can also be generated Computer-Aided Intelligent Recognition Techniques and Applications Edited by M. Sarfraz © 2005 John Wiley ... fatigue. The application results agree with the theoretical results. Computer-Aided Intelligent Recognition Techniques and Applications Edited by M. Sarfraz © 2005 Joh...

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... 469, 471, 4 78 Chaotic component 469, 471, 4 78 Chaotic masking 469, 471, 4 78 Chaotic output 469, 471, 4 78 Chaotic receivers 469, 471, 4 78, 481 Chaotic semiconductor 469, 472, 480 , 482 , 484 Chaotic ... kernels 180 , 4 28 Gaussian mixture models 234, 299 Gaussian noise distribution 351 Gene 382 , 385 Generation 67, 68, 69, 70, 384 , 412 Generation process 67, 68, 69 Generic 33, 34...

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... of medical image. Computer-Aided Intelligent Recognition Techniques and Applications Edited by M. Sarfraz © 2005 John Wiley & Sons, Ltd Empirical Analysis 249 0 20 40 60 80 100 120 20 × 20 ... nature [1] and the lack of definition of the ‘correct’ segmentation [2]. Perhaps as a consequence of these shortcomings, a plethora of Computer-Aided Intelligent Recognition...

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... Recognition, 28( 1), pp. 67 80 , 1995. [11] Lu, Y. and Shridhar, M. “Character segmentation in handwritten words – an overview,” Pattern Recognition, 29(1), pp. 77–96, 1996. [12] Shridhar, M. and ... vectors 1916 2109 6100 7245 12 105 23 710 Number of prototypes 1 08 186 5 58 2 78 723 1577 Prototypes per label ratio 10 87 221527 82 7 86 07 Previous Work 55 Figure 4.4 Segmentation...

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... 89 .74 82 .51 77.33 Yes Prototype 93 .84 87 .81 86 .76 95.04 89 . 68 88. 28 Yes Propinit 88 .47 78. 38 76.71 89 .23 80 .92 83 .49 Yes Eveninit 85 . 08 73.11 75.40 89 .42 80 .02 82 . 28 Yes k-means 93.32 87 . 58 86.34 ... 83 .87 91.12 87 . 28 83.53 No Propinit 75 .85 70.65 67.30 83 .97 75. 78 75.50 No Eveninit 75.74 58. 04 65.25 79.51 70 .86 70.63 No k-means 90.40 85...

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... fingerprint recognition, face recognition, iris recognition, retina recognition, hand geometry, voice recognition and signature recognition, among others. Face recognition, in particular, has ... Wilson, C. L. and Sirohey, S. “Human and machine recognition of faces: A survey,” Proceedings of the IEEE, 83 , pp. 705–740, 1995. [4] Samal, A. and Lyengar, P. A. “Automatic reco...

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... s) Max. recognition accuracy Memory usage View-based LDA 183 650 88 .333 514 320 Traditional LDA 288 719 83 .333 429 200 View-based PCA 90 485 0 84 .444 514 320 Traditional PCA 1409060 85 429 200 6. ... Lee, S. B. and Tsutsui, S. Intelligent Biometric Techniques in Fingerprint and Face Recognition. CRC Press, 1990. [3] Moghaddam, B. and Pentland, A. “Face recognition us...

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