Title :
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
fDate :
3/1/2007 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.891201
