A comparison of fuzzy shell-clustering methods for the detection of ellipses

In this paper, we introduce a shell-clustering algorithm for ellipsoidal clusters based on the so-called "radial distance" which can be easily extended to superquadric clusters. We compare our algorithm with other algorithms in the literature that are based on the algebraic distance, the a...

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Bibliographic Details
Published inIEEE transactions on fuzzy systems Vol. 4; no. 2; pp. 193 - 199
Main Authors Frigui, H., Krishnapuram, R.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.05.1996
Institute of Electrical and Electronics Engineers
Subjects
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial intelligence
Automatic frequency control
Closed-form solution
Clustering algorithms
Computer science; control theory; systems
Computer vision
Curve fitting
Exact sciences and technology
Fuzzy sets
Minimization methods
Pattern recognition. Digital image processing. Computational geometry
Prototypes
Scattering
Size measurement
Theoretical computing
Aggregation
Algorithm
Edge detection
Ellipsoid
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Summary:In this paper, we introduce a shell-clustering algorithm for ellipsoidal clusters based on the so-called "radial distance" which can be easily extended to superquadric clusters. We compare our algorithm with other algorithms in the literature that are based on the algebraic distance, the approximate distance, the normalized radial distance, and the exact distance. We evaluate the performance of each algorithm on two-dimensional data sets containing "scattered" ellipses, partial ellipses, outliers, and ellipses of disparate sizes, and summarize the relative strengths and weaknesses of each algorithm.
ISSN:1063-6706
1941-0034
DOI:10.1109/91.493912