Unsupervised fuzzy clustering with multi-center clusters
Fuzzy Sets and Systems - Clustering and modeling
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A swarm-inspired projection algorithm
Pattern Recognition
Fuzzy Aggregation with Artificial Color filters
Information Sciences: an International Journal
Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Possibilistic shell clustering of template-based shapes
IEEE Transactions on Fuzzy Systems
Clustering: A neural network approach
Neural Networks
An information-theoretic fuzzy C-spherical shells clustering algorithm
Fuzzy Sets and Systems
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
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
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Objective function-based clustering has been generalized recently to detect contours of circles and ellipses or even hyperbolas in a set of binary data vectors. Although there are special algorithms to discover lines, the detection of rectangles needs further treatment. A simple line-detection algorithm is not sufficient for rectangles since for identifying four lines as one rectangle, additional information such as the length of the lines and whether they are parallel or meet at a right angle is necessary. In this paper, a special fuzzy shell-clustering algorithm for rectangular contours is developed. The principal idea behind it can be generalized for other polygons so we also derive an algorithm that is capable of detecting rectangles and other polygons as well as approximating circles, ellipses, and lines