COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 8 pdf

... 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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... (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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... 46 Receiver 1 28, 330, 469, 471 Receiver Operating Curve 1 28 Recognition 1, 1 28, 129, 130, 132 Recognition rates 1 48, 156, 310, 311, 312 Recognition system 100, 146, 1 48, 170 Recognizer 54, 82 , 86 Reconstruction ... 299 Path 53, 58, 68, 395 Pattern classification 88 , 212, 297–9, 422 Pattern classifier 299, 312, 317 Pattern generation 453, 454 Pattern recognition 1, 5, 17, 1...

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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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... 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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... step. 1 2 3 4 5 6 7 S1 S2 S3 S4 S5 S6 S7 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 125 123 113 127 112 125 135 175 179 1 28 127 127 121 132 185 179 176 122 117 111 114 174 159 182 171 152 122 113 183 1 78 185 176 173 139 123 170 192 186 181 177 169 119 187 186 180 181 186 175 183 Figure 15.16 3D pr...

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... of types of mine and of scenarios Computer-Aided Intelligent Recognition Techniques and Applications Edited by M. Sarfraz © 2005 John Wiley & Sons, Ltd 322 Improving Mine Recognition Regarding ... all 59 .85 42.42 Polynomial one vs. one 90.53 55.63 RBF one vs. all 78. 98 51.95 RBF one vs. one 90.34 58. 01 3 28 Improving Mine Recognition This step is repeated some prese...

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