Convergence condition and efficient implementation of the fuzzy curve-tracing (FCT) algorithm

The fuzzy curve-tracing (FCT) algorithm can be used to extract a smooth curve from unordered noisy data. In this paper, we analyze the convergence property of the algorithm based on the diagonal dominance requirement of the matrix used in the clustering procedure and prove that the algorithm is guar...

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Vydané v:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Ročník 34; číslo 1; s. 210 - 221
Hlavný autor: Yan, H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.02.2004
Predmet:
Algorithm design and analysis
Clustering algorithms
Convergence
Councils
Data mining
Image resolution
Iterative algorithms
Skeleton
Stability
ISSN:1083-4419
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Shrnutí:The fuzzy curve-tracing (FCT) algorithm can be used to extract a smooth curve from unordered noisy data. In this paper, we analyze the convergence property of the algorithm based on the diagonal dominance requirement of the matrix used in the clustering procedure and prove that the algorithm is guaranteed to converge if the weighting coefficient for the smoothness constraint is chosen properly. Based on the convergence condition, we develop several methods for fast and reliable implementation of the algorithm. We show that the algorithm can be initialized with a user-defined curve in many cases, that a multiresolution clustering based approach and an image down-sampling scheme can be used to improve the algorithm stability and speed and that two types of traps can be removed to correct the mistakes in curve tracing. We demonstrate several advantages of our algorithm over the commonly used snake models for boundary detection and several methods for principle curve extraction.
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ISSN:1083-4419
DOI:10.1109/TSMCB.2003.811763