Journal ArticleDOI
Robust and adaptive techniques in self-organizing neural networks
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TLDR
Applications that prove the superiority of the proposed variants of LVQ and RBF neural networks in noisy color image segmentation, color-based image recognition, segmentation of ultrasonic images, motion-field smoothing and moving object segmentation are outlined.Abstract:
Robust and adaptive training algorithms aiming at enhancing the capabilities of self-organizing and Radial Basis Function (RBF) neural networks are reviewed in this paper. The following robust variants of Learning Vector Quantizer (LVQ) are described: the order statistics LVQ, the L 2 LVQ and the split-merge LVQ. Successful application of the marginal median LVQ that belongs to the class of order statistics LVQs in the self-organized selection of the centers in RBF neural networks is reported. Moreover, the use of the median absolute deviation in the estimation of the covariance matrix of the observations assigned to each hidden unit in RBF neural networks is proposed. Applications that prove the superiority of the proposed variants of LVQ and RBF neural networks in noisy color image segmentation, color-based image recognition, segmentation of ultrasonic images, motion-field smoothing and moving object segmentation are outlined.read more
Citations
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Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum
TL;DR: This work has provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000.
Journal ArticleDOI
Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators
TL;DR: The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain and provide the basis for evaluating a variety of ordered weighted LVQ And Clustering algorithms.
Journal ArticleDOI
Evaluation of Raman spectra of human brain tumor tissue using the learning vector quantization neural network
TL;DR: The learning vector quantization (LVQ) neural network is a recent approach to excavating Raman spectra information that is fast and convenient, does not require the spectra peak counterpart, and achieves a relatively high accuracy.
Book ChapterDOI
Heart Cavity Segmentation in Ultrasound Images Based on Supervised Neural Networks
TL;DR: This paper proposes a segmentation method of heart cavities based on neural networks that permits detection of cavity contours with techniques of a low computational cost, in a robust and accurate way, with a high degree of autonomy.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
Self Organization And Associative Memory
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
BookDOI
Nonlinear Digital Filters
TL;DR: This chapter discusses digital filters based on order statistics, Morphological image and signal processing, and Adaptive nonlinear filters.
Journal ArticleDOI
A fast two-dimensional median filtering algorithm
Thomas S. Huang,G. Yang,G. Tang +2 more
TL;DR: A fast algorithm for two-dimensional median filtering based on storing and updating the gray level histogram of the picture elements in the window is presented, which is much faster than conventional sorting methods.