Title :
Fuzzy curve-tracing algorithm
Author_Institution :
City Univ. of Hong Kong, Kowloon, China
fDate :
10/1/2001 12:00:00 AM
Abstract :
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
Keywords :
curve fitting; fuzzy logic; handwriting recognition; object detection; average class membership; cluster centers; fuzzy c-means algorithm; fuzzy clustering algorithm; fuzzy curve-tracing algorithm; smooth curve extraction; spatial relations; unordered noisy data; Clustering algorithms; Clustering methods; Data mining; Helium; Humans; Image analysis; Noise shaping; Object detection; Shape; Testing;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/3477.956038
