Abstract
In this paper, we define clusters and the boundary curves of clusters in a random point set using the Delaunay triangulation and the principal curve analysis. The principal curve analysis is a generalization of principal axis analysis, which is a standard method for data analysis in pattern recognition.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Amenta, N., Bern, M., Eppstein, D.: The crust and the β- skeleton:Combinatorial curve reconstruction. Graphical Models and Image Processing 60, 125–135 (1998)
Attali, D., Montanvert, A.: Computing and simplifying 2D and 3D continuous skeletons. CVIU 67, 261–273 (1997)
Edelsbrunner, H.: Shape reconstruction with Delaunay complex. In: Lucchesi, C.L., Moura, A.V. (eds.) LATIN 1998. LNCS, vol. 1380, pp. 119–132. Springer, Heidelberg (1998)
Hasite, T., Stuetzle, T.: Principal curves. J. Am. Statistical Assoc. 84, 502–516 (1989)
Kégl, B., Krzyzak, A., Linder, T., Zeger, K.: Learning and design of principal curves. IEEE PAMI 22, 281–297 (2000)
Oja, E.: Principal components, minor components, and linear neural networks. Neural Networks 5, 927–935 (1992)
Imiya, A., Ootani, H., Kawamoto, K.: Linear manifolds analysis: Theory and algorithm. Neurocomputing 57, 171–187 (2004)
Imiya, A., Kawamoto, K.: Learning dimensionality and orientations of 3D objects. Pattern Recognition Letters 22, 75–83 (2001)
Silverman, B.W.: Some aspects of the spline smoothing approach to nonparametric regression curve fitting. J. R. Statist. Soc, B. 47, 1–52 (1985)
Bookstein, F.L.: The line-skeleton. CVGIP 11, 1233–1237 (1979)
Rosenfeld, A.: Axial representations of shapes. CVGIP 33, 156–173 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Imiya, A., Tatara, K. (2004). Graph-Based Clustering of Random Point Set. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_104
Download citation
DOI: https://doi.org/10.1007/978-3-540-27868-9_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22570-6
Online ISBN: 978-3-540-27868-9
eBook Packages: Springer Book Archive
