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Volumn 19, Issue 9, 2008, Pages

Automatic inline defect detection for a thin film transistor-liquid crystal display array process using locally linear embedding and support vector data description

Author keywords

Defect detection; Locally linear embedding; Manifold learning; One class classification; Support vector data description; Thin film transistor liquid crystal display

Indexed keywords

CRYSTAL DEFECTS; DATA DESCRIPTION; DECISION MAKING; DEFECTS; IMAGE SEGMENTATION; LIQUID CRYSTAL DISPLAYS; LIQUIDS; MANUFACTURE; PIXELS; THIN FILM CIRCUITS; THIN FILMS;

EID: 51549110785     PISSN: 09570233     EISSN: 13616501     Source Type: Journal    
DOI: 10.1088/0957-0233/19/9/095501     Document Type: Article
Times cited : (27)

References (47)
  • 1
    • 38849172631 scopus 로고    scopus 로고
    • Automatic TFT-LCD mura defect inspection using discrete cosine transform-based background filtering and 'just noticeable difference' quantification strategies
    • Chen L-C and Kuo C-C 2008 Automatic TFT-LCD mura defect inspection using discrete cosine transform-based background filtering and 'just noticeable difference' quantification strategies Meas. Sci. Technol. 19 015507
    • (2008) Meas. Sci. Technol. , vol.19 , Issue.1 , pp. 015507
    • Chen, L.-C.1    Kuo, C.-C.2
  • 2
    • 33847133475 scopus 로고    scopus 로고
    • A fuzzy neural network approach for quantitative evaluation of mura in TFT-LCD 2005
    • Zhang Y and Zhang J 2005 A fuzzy neural network approach for quantitative evaluation of mura in TFT-LCD 2005 Int. Conf. on Neural Networks and Brain pp 424-7
    • (2005) Int. Conf. on Neural Networks and Brain , vol.1 , pp. 424-427
    • Zhang, Y.1    Zhang, J.2
  • 4
    • 7544230477 scopus 로고    scopus 로고
    • Automatic detection of region-mura defect in TFT-LCD
    • Lee Y J and Yoo S I 2004 Automatic detection of region-mura defect in TFT-LCD IEICE Trans. Inf. Syst. E87-D 2371-8
    • (2004) IEICE Trans. Inf. Syst. , vol.87-500 , pp. 2371-2378
    • Lee, Y.J.1    Yoo, S.I.2
  • 5
    • 8644232477 scopus 로고    scopus 로고
    • Detection of spot-type defects on liquid crystal display modules
    • Kim W S, Kwak D M, Song Y C, Choi D H and Park K H 2004 Detection of spot-type defects on liquid crystal display modules Key Eng. Mater. 270 808-13
    • (2004) Key Eng. Mater. , vol.270 , pp. 808-813
    • Kim, W.S.1    Kwak, D.M.2    Song, Y.C.3    Choi, D.H.4    Park, K.H.5
  • 6
    • 8644280412 scopus 로고    scopus 로고
    • Line defect detection in TFT-LCD using directional filter bank and adaptive multilevel thresholding
    • Oh J H, Kwak D M, Lee K B, Song Y C, Choi D H and Park K H 2004 Line defect detection in TFT-LCD using directional filter bank and adaptive multilevel thresholding Key Eng. Mater. 270 233-8
    • (2004) Key Eng. Mater. , vol.270 , pp. 233-238
    • Oh, J.H.1    Kwak, D.M.2    Lee, K.B.3    Song, Y.C.4    Choi, D.H.5    Park, K.H.6
  • 7
    • 6344253017 scopus 로고    scopus 로고
    • Multiscale detection of defect in thin film transistor liquid crystal display panel
    • Song Y C, Choi D H and Park K H 2004 Multiscale detection of defect in thin film transistor liquid crystal display panel Japan. J. Appl. Phys. 43 5465-8
    • (2004) Japan. J. Appl. Phys. , vol.43 , pp. 5465-5468
    • Song, Y.C.1    Choi, D.H.2    Park, K.H.3
  • 8
    • 11244318311 scopus 로고    scopus 로고
    • Quantitative evaluation of mura in liquid crystal
    • Mori Y, Tanahashi K and Tsuji S 2004 Quantitative evaluation of mura in liquid crystal Opt. Eng. 43 2696-700
    • (2004) Opt. Eng. , vol.43 , Issue.11 , pp. 2696-2700
    • Mori, Y.1    Tanahashi, K.2    Tsuji, S.3
  • 10
    • 13444257666 scopus 로고    scopus 로고
    • An anisotropic diffusion-based defect devotion for sputtered surfaces with inhomogeneous textures
    • Tsai D-M and Chao S-M 2005 An anisotropic diffusion-based defect devotion for sputtered surfaces with inhomogeneous textures Image Vis. Underst. 23 325-38
    • (2005) Image Vis. Underst. , vol.23 , pp. 325-338
    • Tsai, D.-M.1    Chao, S.-M.2
  • 11
    • 33745047543 scopus 로고    scopus 로고
    • An independent component analysis-based filter design for defect detection in low-contrast surface images
    • Tsai D-M, Lin P-C and Lu C-J 2006 An independent component analysis-based filter design for defect detection in low-contrast surface images Pattern Recognit. 39 1679-94
    • (2006) Pattern Recognit. , vol.39 , Issue.9 , pp. 1679-1694
    • Tsai, D.-M.1    Lin, P.-C.2    Lu, C.-J.3
  • 12
    • 11144285420 scopus 로고    scopus 로고
    • Automatic defect inspection for LCDs using singular value decomposition
    • Lu C-J and Tsai D-M 2004 Automatic defect inspection for LCDs using singular value decomposition Int. J. Adv. Manuf. Technol. 25 53-61
    • (2004) Int. J. Adv. Manuf. Technol. , vol.25 , Issue.1-2 , pp. 53-61
    • Lu, C.-J.1    Tsai, D.-M.2
  • 14
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • Burges C J C 1998 A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discovery 2 121-67
    • (1998) Data Min. Knowl. Discovery , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.C.1
  • 16
    • 33750344925 scopus 로고    scopus 로고
    • Face detection using kernel PCA and imbalanced SVM
    • Liu Y-H and Chen Y-T 2006 Face detection using kernel PCA and imbalanced SVM Advances in Natural Computation (Lecture Notes in Computer Science vol 4221) (Berlin: Springer) pp 351-60
    • (2006) Advances in Natural Computation , vol.4221 , pp. 351-360
    • Liu, Y.-H.1    Chen, Y.-T.2
  • 17
    • 33846063260 scopus 로고    scopus 로고
    • Face recognition using total margin-based adaptive fuzzy support vector machines
    • Liu Y-H and Chen Y-T 2007 Face recognition using total margin-based adaptive fuzzy support vector machines IEEE Trans. Neural Netw. 18 178-92
    • (2007) IEEE Trans. Neural Netw. , vol.18 , Issue.1 , pp. 178-192
    • Liu, Y.-H.1    Chen, Y.-T.2
  • 18
    • 0036477056 scopus 로고    scopus 로고
    • Face recognition using kernel principal component analysis
    • Kim K I, Jung K and Kim H J 2002 Face recognition using kernel principal component analysis IEEE Signal Process. Lett. 9 40-2
    • (2002) IEEE Signal Process. Lett. , vol.9 , Issue.2 , pp. 40-42
    • Kim, K.I.1    Jung, K.2    Kim, H.J.3
  • 19
    • 34347378883 scopus 로고    scopus 로고
    • Recognition of electromyographic signals using cascaded kernel learning machine
    • Liu Y-H, Huang H-P and Weng C-H 2007 Recognition of electromyographic signals using cascaded kernel learning machine IEEE/ASME Trans. Mechatronics 12 253-64
    • (2007) IEEE/ASME Trans. Mechatronics , vol.12 , Issue.3 , pp. 253-264
    • Liu, Y.-H.1    Huang, H.-P.2    Weng, C.-H.3
  • 20
    • 0142063407 scopus 로고    scopus 로고
    • Novelty detection: A review: Part I. Statistical approaches
    • Markou M and Singh S 2003 Novelty detection: a review: Part I. Statistical approaches Signal Process. 83 2481-97
    • (2003) Signal Process. , vol.83 , Issue.12 , pp. 2481-2497
    • Markou, M.1    Singh, S.2
  • 21
    • 0142126712 scopus 로고    scopus 로고
    • Novelty detection: A review: Part II. Neural network based approaches
    • Markou M and Singh S 2003 Novelty detection: a review: Part II. Neural network based approaches Signal Process. 83 2499-521
    • (2003) Signal Process. , vol.83 , Issue.12 , pp. 2499-2521
    • Markou, M.1    Singh, S.2
  • 22
    • 8844281752 scopus 로고    scopus 로고
    • Novelty detection in learning systems
    • Marsland S 2003 Novelty detection in learning systems Neural Comput. Surv. 3 157-95
    • (2003) Neural Comput. Surv. , vol.3 , pp. 157-195
    • Marsland, S.1
  • 23
    • 0942266514 scopus 로고    scopus 로고
    • Support vector data description
    • Tax D and Duin R 2004 Support vector data description Mach. Learn. 54 45-66
    • (2004) Mach. Learn. , vol.54 , Issue.1 , pp. 45-66
    • Tax, D.1    Duin, R.2
  • 24
    • 33746885881 scopus 로고    scopus 로고
    • A support vector method for anomaly detection in hyperspectral imagery
    • Banerjee A, Burlina P and Diehl C 2006 A support vector method for anomaly detection in hyperspectral imagery IEEE Trans. Geosci. Remote Sens. 44 2282-91
    • (2006) IEEE Trans. Geosci. Remote Sens. , vol.44 , Issue.8 , pp. 2282-2291
    • Banerjee, A.1    Burlina, P.2    Diehl, C.3
  • 26
    • 32544438305 scopus 로고    scopus 로고
    • Machine learning algorithms for T-cell epitopes prediction
    • Nanni L 2006 Machine learning algorithms for T-cell epitopes prediction Neurocomputing 69 866-8
    • (2006) Neurocomputing , vol.69 , Issue.7-9 , pp. 866-868
    • Nanni, L.1
  • 28
    • 0037276932 scopus 로고    scopus 로고
    • Face recognition using kernel direct discriminant analysis algorithms
    • Lu J, Plataniotis K N and Venetsanopoulos A N 2003 Face recognition using kernel direct discriminant analysis algorithms IEEE Trans. Neural Netw. 14 117-26
    • (2003) IEEE Trans. Neural Netw. , vol.14 , Issue.1 , pp. 117-126
    • Lu, J.1    Plataniotis, K.N.2    Venetsanopoulos, A.N.3
  • 29
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • Schölkopf B, Smola A and Müller K R 1998 Nonlinear component analysis as a kernel eigenvalue problem Neural Comput. 10 1299-319
    • (1998) Neural Comput. , vol.10 , Issue.5 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.2    Müller, K.R.3
  • 30
    • 33750522220 scopus 로고    scopus 로고
    • Kernel PCA for novelty detection
    • Hoffmann H 2007 Kernel PCA for novelty detection Patt. Recognit. 40 863-74
    • (2007) Patt. Recognit. , vol.40 , Issue.3 , pp. 863-874
    • Hoffmann, H.1
  • 31
    • 0025489075 scopus 로고
    • The self-organizing map
    • Kohonen T 1990 The self-organizing map Proc. IEEE 78 1464-80
    • (1990) Proc. IEEE , vol.78 , Issue.9 , pp. 1464-1480
    • Kohonen, T.1
  • 32
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis S T and Saul L K 2000 Nonlinear dimensionality reduction by locally linear embedding Science 290 2323-6
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 33
    • 2342517502 scopus 로고    scopus 로고
    • Think globally, fit locally: Unsupervised learning of low dimensional manifolds
    • Saul L K and Roweis S T 2004 Think globally, fit locally: unsupervised learning of low dimensional manifolds J. Mach. Learn. Res. 4 119-55
    • (2004) J. Mach. Learn. Res. , vol.4 , Issue.2 , pp. 119-155
    • Saul, L.K.1    Roweis, S.T.2
  • 34
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • Tenenbaum J B, Silva V and Langford J C 2000 A global geometric framework for nonlinear dimensionality reduction Science 290 2319-23
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenenbaum, J.B.1    Silva, V.2    Langford, J.C.3
  • 35
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • Belkin M and Niyogi P 2003 Laplacian eigenmaps for dimensionality reduction and data representation Neural Comput. 15 1373-96
    • (2003) Neural Comput. , vol.15 , Issue.6 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 38
    • 15344349594 scopus 로고    scopus 로고
    • Face membership authentication using SVM classification tree generated by membership-based LLE data partition
    • Pang S N, Kim D and Bang S Y 2005 Face membership authentication using SVM classification tree generated by membership-based LLE data partition IEEE Trans. Neural Netw. 16 436-46
    • (2005) IEEE Trans. Neural Netw. , vol.16 , Issue.2 , pp. 436-446
    • Pang, S.N.1    Kim, D.2    Bang, S.Y.3
  • 40
    • 0018306059 scopus 로고
    • A threshold selection method from gray-level histograms
    • Otsu N 1979 A threshold selection method from gray-level histograms IEEE Trans. Syst. Man Cybern. 9 62-9
    • (1979) IEEE Trans. Syst. Man Cybern. , vol.9 , Issue.1 , pp. 62-69
    • Otsu, N.1
  • 43
    • 0036505670 scopus 로고    scopus 로고
    • A comparison of methods for multiclass support vector machines
    • Hsu C-W and Lin C-J 2002 A comparison of methods for multiclass support vector machines IEEE Trans. Neural Netw. 13 415-25
    • (2002) IEEE Trans. Neural Netw. , vol.13 , Issue.2 , pp. 415-425
    • Hsu, C.-W.1    Lin, C.-J.2
  • 44
    • 0033220728 scopus 로고    scopus 로고
    • Support vector domain description
    • Tax D and Duin R 1999 Support vector domain description Pattern Recognit. Lett. 20 1191-9
    • (1999) Pattern Recognit. Lett. , vol.20 , Issue.11-13 , pp. 1191-1199
    • Tax, D.1    Duin, R.2
  • 45
  • 47
    • 0345307577 scopus 로고    scopus 로고
    • EMG classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps
    • Huang H-P, Liu Y-H, Liu L-W and Wong C-S 2003 EMG classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps IEEE Int. Conf. Robotics and Automation (ICRA'03) vol 1 pp 1497-502
    • (2003) IEEE Int. Conf. Robotics and Automation (ICRA'03) , vol.1 , pp. 1497-1502
    • Huang, H.-P.1    Liu, Y.-H.2    Liu, L.-W.3    Wong, C.-S.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.