Neurocomputing Model for Computation of an Approximate Convex Hull of a Set of Points and Spheres

Author

Pal, Shovon ; Hattacharya, S.

Author_Institution

Electron. & Commun. Sci. Unit, Indian Stat. Inst., Calcutta

Volume

18

Issue

2

fYear

2007

fDate

3/1/2007 12:00:00 AM

Firstpage

600

Lastpage

605

Abstract

In this letter, a two-layer neural network is proposed for computation of an approximate convex hull of a set of given points in 3-D or a set of spheres of different sizes. The algorithm is designed based on an elegant concept-shrinking of a spherical rubber balloon surrounding the set of objects in 3-D. Logically, a set of neurons is orderly placed on a spherical mesh i.e., on a rubber balloon surrounding the objects. Each neuron has a parameter vector associated with its current position. The resultant force of attraction between a neuron and each of the given points/objects, determines the direction of a movement of the neuron lying on the rubber balloon. As the network evolves, the neurons (parameter vectors) approximate the convex hull more and more accurately

Keywords

approximation theory; neural nets; approximate convex hull; neurocomputing model; spherical rubber balloon; two-layer neural network; Computational modeling; Force measurement; Shape; Convex hull; energy function; neural networks; Algorithms; Artificial Intelligence; Computer Simulation; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2007.891201

Filename

4118274