Parametric subspace modeling of speech transitions
Speech Communication
Learning and Design of Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Curvilinear Features in Spatial Point Patterns: Principal Curve Clustering with Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Another look at principal curves and surfaces
Journal of Multivariate Analysis
Piecewise Linear Skeletonization Using Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A k-segments algorithm for finding principal curves
Pattern Recognition Letters
Principal curves with bounded turn
IEEE Transactions on Information Theory
Self-organizing maps for the skeletonization of sparse shapes
IEEE Transactions on Neural Networks
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This paper proposes a new method for finding principal curves from data sets. Motivated by solving the problem of highly curved and self-intersecting curves, we present a bottom-up strategy to construct a graph called a principal graph for representing a principal curve. The method initializes a set of vertices based on principal oriented points introduced by Delicado, and then constructs the principal graph from these vertices through a two-layer iteration process. In inner iteration, the kernel smoother is used to smooth the positions of the vertices. In outer iteration, the principal graph is spanned by minimum spanning tree and is modified by detecting closed regions and intersectional regions, and then, new vertices are inserted into some edges in the principal graph. We tested the algorithm on simulated data sets and applied it to image skeletonization. Experimental results show the effectiveness of the proposed algorithm.