Strip line detection and thinning by RPCL-based local PCA

Pattern Recognition Letters

Volumn 24, Issue 14, 2003, Pages 2335-2344

  Liu, Zhi Yong ^a   Chiu, Kai Chun ^a   Xu, Lei ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Local PCA; Model selection; RPCL learning; Strip line detection; Strip line thinning

Indexed keywords

ALGORITHMS; IMAGE PROCESSING; STATISTICAL METHODS; STRIP TELECOMMUNICATION LINES;

STRIP LINE DETECTION;

PATTERN RECOGNITION;

IMAGE PROCESSING; PATTERN RECOGNITION;

EID: 0042842439     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(03)00059-X     Document Type: Article

References (18)

1. Ballard D. Generalizing the Hough transform to detect arbitrary shapes Pattern Recognition 13 2 1981 111-122
2. Davies E.R. Plummer A.P.N. Thinning algorithms: A critique and a new methodology Pattern Recognition 14 1981 53-63
3. Desieno D. Adding a conscience to competitive learning Proc. IEEE Internat. Conf. on Neural Networks I 1988 117-124
4. Hinton G. Revow M. Dayan P. Recognizing handwritten digits using mixtures of linear models Adv. Neural Inform. Process. System 7 1995 1015-1022
5. Hough, P., 1962. Method and means for recognizing complex patterns. US Patent 3069654, 18 December 1962
6. Jolliffe I.T. Principal Component Analysis 1986 Springer-Verlag New York
7. Kambhatla N. Leen T. Dimension reduction by local principal component analysis Neural Comput. 9 1997 1493-1516
8. Lo R.C. Tsai W.H. Gray-scale Hough transform for thick line detection in gray-scale images Pattern Recognition 28 1995 647-661
9. Marchand-Maillet S. Sharaiha Y.M. Binary Digital Image Processing: A Discrete Approach 2000 Academic Press
10. McCafferty J.D. Human and Machine Vision 1990 Ellis Horwood
11. Otsu U. A threshold selection method from gray-level histograms IEEE Trans. Systems Man Cybernet. 9 1 1979 62-67
12. Tipping M. Bishop C. Mixtures of probabilistic principal component analysis Neural Comput. 11 1999 443-482
13. Xu, L., 1994. Multisets modeling learning: An unified theory for supervised and unsupervised learning. Proc. 1994 IEEE Internat. Conf. on Neural Networks I, 315-320
14. Xu, L., 1998. Rival penalized competitive learning, finite mixture, and multisets clustering. Proc. IEEE-INNS Internat. Joint Conf. on Neural Networks II, 2525-2530
15. Xu L. An overview on unsupervised learning from data mining perspective Nigel Allison et-al Advances in Self-Organizing Maps 2001 181-210 Springer-Verlag
16. Xu L. Krzyzak A. Oja E. Rival penalized competitive learning for clustering analysis, RBF net, and curve detection IEEE Trans. on Neural Networks 4 1993 636-649
17. Xu L. Oja E. Randomized Hough transform (RHT): Basic mechanisms, algorithms and complexities Comput. Vision Graphics Image Process. Image Understanding 57 2 1993 131-154
18. Xu L. Oja E. Kultanen P. A new curve detection method: Randomized Hough transform (RHT) Pattern Recognition Lett. 11 1990 331-338