A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organization of Pulse-Coupled Oscillators with Application to Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Irregular Shapes Using High-Order Neurons
Neural Computation
Possibilistic shell clustering of template-based shapes
IEEE Transactions on Fuzzy Systems
An Adaptive k-Nearest Neighbors Clustering Algorithm for Complex Distribution Dataset
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
A fast and robust ellipse detection algorithm based on pseudo-random sample consensus
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Possibilistic c-template clustering and its application in object detection in images
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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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