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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... (Update)
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BibTeX entry: (Update)
A. G. Bors, I. Pitas, "Alpha-trimmed Mean Radial Basis Functions and their Application in Object Modeling," CD-ROM Proc of the IEEE Workshop on Nonlinear Signal and Image Processing (NSIP'97), Michigan, USA, 7-11 Sept. 1997. http://citeseer.ist.psu.edu/bors97alphatrimmed.html More
@misc{ bors97alphatrimmed,
author = "A. Bors and I. Pitas",
title = "Alpha-trimmed Mean Radial Basis Functions and their Application in Object
Modeling",
text = "A. G. Bors, I. Pitas, Alpha-trimmed Mean Radial Basis Functions and their
Application in Object Modeling, CD-ROM Proc of the IEEE Workshop on Nonlinear
Signal and Image Processing (NSIP'97), Michigan, USA, 7-11 Sept. 1997.",
year = "1997",
url = "citeseer.ist.psu.edu/bors97alphatrimmed.html" }
Citations (may not include all citations):463 Computer and Robot Vision (context) - Haralick, Shapiro - 1992
52 Rival penalized competitive learning for clustering analysis.. (context) - Xu, Krzyzak et al. - 1993
18 Median radial basis function neural network (context) - Bor, Pitas - 1996
4 Some statistical properties of Alpha-Trimmed mean and standa.. (context) - Peterson, Lee et al. - 1988
3 Principles and Applications (context) - Pitas, Venetsanopoulos et al. - 1989
3 Selection of trimming proportions for robust adaptive trimme.. (context) - Prescott - 1978
2 Shape description and representation by ellipsoids (context) - Kimoto, Yasuda - 1997
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