How to discriminate shapes using shape vector
Pattern Recognition Letters
Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Density-weighted fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
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This article presents the development of nanoparticles (NPs) with potentially useful size and shape dependent properties that have the advantage of ultra-fine size, high surface area and useful interfacial imperfections. When developing NPs as catalysts, their shape is very important. For a certain volume of material, nanoparticles make the best catalysts when they have a large surface area. It is a challenge to find the shape that has the largest surface area for its volume. The particle shape contours were measured by transmission electron microscope with high resolution. These TEM images are analyzed with image clustering techniques and generalized shape theory that results the computational indicators for shape, degree of atomic compactness and charge arrangement of NPs.