Bors, A G and Pitas, I (1999) Object classification in 3-D images using alpha-trimmed mean radial basis function network. IEEE Transactions on Image Processing. pp. 1744-1756. ISSN 1057-7149Full text available as:
We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. The segmentation relies on the geometrical model and graylevel statistics, The characteristic parameters of the ellipsoids and of the graylevel statistics are embedded in a radial basis function (RBF) network and they are found by means of unsupervised training, 4 new robust training algorithm for RBF networks based on alpha-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing spherical coordinate system is used for ellipsoidal center estimation. We study the performance of the proposed algorithm and we present results when segmenting a stack of microscopy images.
|Copyright, Publisher and Additional Information:||Copyright © 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Keywords:||alpha-trimmed mean, radial basis function networks, 3-D Hough transform, RESONANCE BRAIN IMAGES, 3-DIMENSIONAL SEGMENTATION, MODEL, RECONSTRUCTION|
|Academic Units:||The University of York > Computer Science (York)|
|Depositing User:||Adrian G. Bors|
|Date Deposited:||20 Jan 2006|
|Last Modified:||17 Oct 2013 14:38|