IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some aspects of the parallel and distributed iterative algorithms—a survey
Automatica (Journal of IFAC)
The Fuzzy C Quadratic Shell clustering algorithm and the detection of second-degree curves
Pattern Recognition Letters
Fuzzy clustering of elliptic ring-shaped clusters
Pattern Recognition Letters
Norm-induced shell-prototypes (NISP) clustering
Neural, Parallel & Scientific Computations
Deformable template models: a review
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised possibilistic clustering
Pattern Recognition
Detecting and measuring rings in banknote images
Engineering Applications of Artificial Intelligence
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
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
A comparison of fuzzy shell-clustering methods for the detection of ellipses
IEEE Transactions on Fuzzy Systems
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Fuzzy shell clustering algorithms in image processing: fuzzy C-rectangular and 2-rectangular shells
IEEE Transactions on Fuzzy Systems
Convex-set-based fuzzy clustering
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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In this paper, we present a new type of alternating-optimization-based possibilistic c-shell algorithm for clustering-template-based shapes. A cluster prototype consists of a copy of the template after translation, scaling, rotation, and/or affine transformations. This extends the capability of shell clustering beyond a few standard geometrical shapes that have been in the literature so far. We use a number of 2-D datasets, consisting of both synthetic and real-world images, to illustrate the capability of our algorithm in detecting generic-template-based shapes in images. We also describe a progressive clustering procedure aimed to relax the requirements for a known number of clusters and good initialization, as well as new performance measures of shell-clustering algorithms.