Shape classification using smooth principal components

Pattern Recognition Letters

Volumn 24, Issue 12, 2003, Pages 2021-2030

  Glendinning, R H ^a   Herbert, R A ^a  

^a QINETIQ   (United Kingdom)

Author keywords

Random trigonometric polynomial; Sample spectral function; Shape classification; Smooth functional principal components

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS; INVARIANCE; PRINCIPAL COMPONENT ANALYSIS; TIME SERIES ANALYSIS;

CROSS-VALIDATION;

CLASSIFICATION (OF INFORMATION);

IMAGE CLASSIFICATION;

EID: 0038329316     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(03)00040-0     Document Type: Article

References (37)

1. Alsberg B.K. Representation of spectra by continuous functions J. Chemom. 7 1993 177 193
2. Beamish N. Priestley M.B. A study of AR and window spectral estimation J. Roy. Statist. Soc. Ser. C 30 1981 41 58
3. Bjørde, O.N., Stenseth, N.C., Saitoh, T., Lingjærde O.C. Mapping the regional transition to cyclicity in Clethrionomys rufocanus: Spectral densities and functional data analysis Res. Popul. Ecol. 40 1998 77 84
4. Cardot H. Non-parametric estimation of smoothed principal components analysis of sampled noisy functions J. Nonparametric Statist. 12 2000 503 538
5. Castro P.E. Lawton W.H. Sylvestre E.A. Principal modes of variation for processes with continuous sample curves Technometrics 28 1986 329 337
6. Champely S. Guinand B. Thioulouse J. Clermidy A. Functional data analysis of curve asymmetry with application to the color pattern of Hydropsyche contubernalis head capsule Biometrics 53 1997 294 305
7. Couvreur C. Fontaine V. Gaunard P. Mubikangiey C.G. Automatic classification of environmental noise events by hidden Markov models Appl. Acoust. 54 1998 187 206
8. Diggle P.J. Al Wasel I. Spectral analysis of replicated biomedical time series J. Roy. Statist. Soc. Ser. C 46 1997 31 71
9. Do K.A. Kirk K. Discriminant analysis of event-related potential curves using smoothed principal components Biometrics 55 1999 174 181
10. Drolon H. Druaux F. Faure A. Particles shape analysis and classification using the wavelet transform Pattern Recognition Lett. 21 2000 473 482
11. Eom K.B. Shape recognition using spectral features Pattern Recognition Lett. 19 1998 189 195
12. Glendinning R.H. Testing for a jump in the periodogram J. Statist. Comput. Simul. 56 1997 117 144
13. Glendinning R.H. Robust shape classification Signal Process 77 1999 121 138
14. Glendinning R.H. Goode A.J. Semiparametric classification of noisy curves Pattern Recognition 36 2003 35 44
15. Greenshields I.R. Rosiene J.A. A fast wavelet-based Karhunen-Loève transform Pattern Recognition 31 1998 839 845
16. Hall P. Poskitt D.S. Presnell B. A functional data-analytic approach to signal discrimination Technometrics 43 2001 1 9
17. Hastie T. Buja A. Tibshirani R. Penalized discriminant analysis Ann. Statist. 23 1995 73 102
18. Hastie T. Ikeda D. Tibshirani R. Statistical measures for the computer-aided diagnosis of mammographic masses J. Comput. Graph. Statist. 8 1999 531 543
19. Ifarraguerri A. Chang C.I. Unsupervised hyperspectral image analysis with projection pursuit IEEE Trans. Geosci. Remote Sensing 38 2000 2529 2538
20. Jaggi S. Karl W.C. Mallat S.G. Willsky A.S. Silhouette recognition using high-resolution pursuit Pattern Recognition 32 1999 753 771
21. Jain A.K. Zhong Y. Dubuisson-Jolly M.P. Deformable template models: A review Signal Process 71 1998 109 129
22. James G.M. Hastie T.J. Functional linear discriminant analysis for irregularly sampled curves J. Roy. Statist. Soc. Ser. B 63 2001 533 550
23. Kauppinen, H., Seppänen, T., Pietikäinen, M., An experimental comparison of autoregressive and Fourier based descriptors in 2D shape classification IEEE Trans. Pattern Anal Machine Intell. 17 1995 201 207
24. Kneip A. Nonparametric estimation of common regressors for similar curve data Ann. Statist. 22 1994 1386 1427
25. Kneip A. Utikal K.J. Inference for density families using functional principal component analysis J. Amer. Statist. Assoc. 96 2001 519 542
26. Kurita T. Sekita I. Otsu N. Invariant distance measures for planar shapes based on complex autoregressive model Pattern Recognition 27 1994 903 911
27. Loncaric S. A survey of shape analysis techniques Pattern Recognition 31 1998 983 1001
28. Murase H. Sakai R. Moving object recognition in eigenspace representation: Gait analysis and lip reading Pattern Recognition Lett. 17 1996 155 162
29. Pinkowski B. Multiscale Fourier descriptors for classifying semivowels in spectrograms Pattern Recognition 26 1993 1593 1602
30. Prakash M. Murty M.N. A genetic approach for selection of (near-)optimal subsets of principal components for discrimination Pattern Recognition Lett. 16 1995 781 787
31. ve transform using a Bayesian approach IEE Proc. Vision Image Signal Process 144 1997 116 123
32. Ramsay J.O. Silverman B.W. Functional Data Analysis 1997 Springer-Verlag New York
33. Rao C.R. Prediction of growth curve models (with discussion) Statist. Sci. 2 1987 434 471
34. Silverman B.W. Smoothed functional principal components analysis by choice of norm Ann. Statist. 24 1996 1 24
35. Singer, P.F., Chellappa, R., 1985. Machine perception of partially specified planar shapes. Proc. IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition, San Francisco, CA, 10-13 June, pp. 497-502
36. Tatum L.G. Hurvich C.M. High breakdown methods for time series analysis J. Roy. Statist. Soc. Ser. B 55 1993 881 896
37. Von Sachs R. Estimating the spectrum of a stochastic process in the presence of a contaminating signal IEEE Trans. Signal Process 41 1993 323 333