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Universal approximation using radial-basis-function networks
Neural Computation
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Segmentation of petrographical images of marbles
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Hierarchical radial basis function neural networks for classification problems
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On the use of different speech representations for speaker modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatic system for quality-based classification of marble textures
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Password Authentication Using Hopfield Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
Multiscale approximation with hierarchical radial basis functions networks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Robotics and Computer-Integrated Manufacturing
Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy
Computer Methods and Programs in Biomedicine
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Marble quality classification is an important procedure generally performed by human experts. However, using human experts for classification is error prone and subjective. Therefore, automatic and computerized methods are needed in order to obtain reproducible and objective results. Although several methods are proposed for this purpose, we demonstrate that their performance is limited when dealing with diverse datasets containing a large number of quality groups. In this work, we test several feature sets and neural network topologies to obtain a better classification performance. During these tests, it is observed that different feature sets represent different subgroup(s) in a quality group rather than representing the whole group. Therefore, our approach is to use these features in a cascaded manner in which a quality group is classified by classifying all of its subgroups. We first realize this approach by using a two-stage cascaded network. Then, we design a hierarchical radial basis function network (HRBFN) in which correctly classified marble samples are taken out of the dataset and a different feature extraction method is applied to the remaining samples at each network level. The HRBFN system produces successful results for industrial applications and facilitates the desirable property of implementation in a quasi real-time manner.