1 February 1992 New fuzzy shell clustering algorithms for boundary detection and pattern recognition
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Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992); doi: 10.1117/12.57082
Event: Robotics '91, 1991, Boston, MA, United States
Abstract
In this paper, we introduce new hard and fuzzy clustering algorithms called the c-quadric shells (CQS) algorithms. These algorithms are specifically designed to seek clusters that can be described by segments of second-degree curves, or more generally by segments of shells of hyperquadrics. Previous shell clustering algorithms have considered clusters of specific shapes such as circles (the fuzzy c-shells algorithm) or ellipses (the fuzzy c-ellipsoids algorithm). The advantage of our algorithm lies in the fact that it can be used to cluster mixtures of all types of hyperquadrics such as hyperspheres, hyperellipsoids, hyperparaboloids, hyperhyperboloids, and even hyperplanes. Several examples of clustering in the two-dimensional case are shown.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Raghu J. Krishnapuram, Hichem Frigui, Olfa Nasraoui, "New fuzzy shell clustering algorithms for boundary detection and pattern recognition", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57082; https://doi.org/10.1117/12.57082
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KEYWORDS
Fuzzy logic

Distance measurement

Detection and tracking algorithms

Evolutionary algorithms

Computer vision technology

Machine vision

Pattern recognition

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