Fuzzy curve-tracing algorithm

This paper presents a fuzzy clustering algorithm for the extraction of a smooth curve from unordered noisy data. In this method, the input data are first clustered into different regions using the fuzzy c-means algorithm and each region is represented by its cluster center. Neighboring cluster cente...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 31; no. 5; pp. 768 - 780
Main Author: Hong, Y
Format: Journal Article
Language:English
Published: United States IEEE 01.10.2001
Subjects:
Algorithms
Clustering
Clustering algorithms
Clustering methods
Clusters
Cybernetics
Data mining
Fuzzy
Fuzzy logic
Fuzzy set theory
Graphs
Helium
Humans
Image analysis
Noise shaping
Object detection
Shape
Testing
ISSN:1083-4419
Online Access:Get full text
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Summary:This paper presents a fuzzy clustering algorithm for the extraction of a smooth curve from unordered noisy data. In this method, the input data are first clustered into different regions using the fuzzy c-means algorithm and each region is represented by its cluster center. Neighboring cluster centers are linked to produce a graph according to the average class membership values. Loops in the graph are removed to form a curve according to spatial relations of the cluster centers. The input samples are then reclustered using the fuzzy c-means (FCM) algorithm, with the constraint that the curve must be smooth. The method has been tested with both open and closed curves with good results.
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ISSN:1083-4419
DOI:10.1109/3477.956038