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Alpha-Trimmed Mean Radial Basis Functions And Their Application In Object Modeling (1997)
| Venue: | CD-ROM Proc of the IEEE Workshop on Nonlinear Signal and Image Processing (NSIP'97 |
| Citations: | 2 - 2 self |
BibTeX
@INPROCEEDINGS{Bors97alpha-trimmedmean,
author = {Adrian G. Bors and Ioannis Pitas and Adrian G. Bor��s Ioannis Pitas},
title = {Alpha-Trimmed Mean Radial Basis Functions And Their Application In Object Modeling},
booktitle = {CD-ROM Proc of the IEEE Workshop on Nonlinear Signal and Image Processing (NSIP'97},
year = {1997},
pages = {7--11}
}
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Abstract
In this paper we use Radial Basis Function (RBF) networks for object modeling in images. An object is composed from a set of overlapping ellipsoids and has assigned an output unit in the RBF network. Each basis function can be geometrically represented by an ellipsoid. We introduce a new robust statistics based algorithm for training radial basis function networks. This algorithm relies on ff-trimmed mean statistics. The use of the proposed algorithm in estimating ellipse parameters is analyzed. 1. INTRODUCTION Radial basis function neural network consists of a two layer feed-forward structure employed for functional approximation and classification proposes. When used in pattern classification an RBF network successfully approximates the Bayesian classifier [1, 2]. In this case, the underlying probability functions are decomposed in a sum of kernel functions with localized support. The functions, implemented by the hidden units, are usually chosen as Gaussian. The intersection of an...
Keyphrases
object modeling alpha-trimmed mean radial basis function rbf network localized support functional approximation new robust statistic classification proposes layer feed-forward structure ff-trimmed mean statistic basis function kernel function output unit pattern classification bayesian classifier radial basis function network algorithm relies radial basis function hidden unit ellipse parameter underlying probability function
