Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: Gray-scale morphological associative memories
Author: Sussner, P
Valle, ME
Abstract: Neural models of associative memories are usually concerned with the storage and the retrieval of binary or bipolar patterns. Thus far, the emphasis in research on morphological associative memory systems has been on binary models, although a number of notable features of autoassociative morphological memories (AMMs) such as optimal absolute storage capacity and one-step convergence have been shown to hold in the general, gray-scale setting. In this paper, we make extensive use of minimax algebra to analyze gray-scale autoassociative morphological memories. Specifically, we provide a complete characterization of the fixed points and basins of attractions which allows us to describe the storage and recall mechanisms of gray-scale AMMs. Computer simulations using gray-scale images illustrate our rigorous mathematical results on the storage capacity and the noise tolerance of gray-scale morphological associative memories (MAMs). Finally, we introduce a modified gray-scale AMM model that yields a fixed point which is closest to the input pattern with respect to the Chebyshev distance and show how gray-scale AMMs can be used as classifiers.
Subject: basin of attraction
fixed point
gray-scale morphological associative memory
minimax algebra
morphological neural network
Country: EUA
Editor: Ieee-inst Electrical Electronics Engineers Inc
Citation: Ieee Transactions On Neural Networks. Ieee-inst Electrical Electronics Engineers Inc, v. 17, n. 3, n. 559, n. 570, 2006.
Rights: fechado
Identifier DOI: 10.1109/TNN.2006.873280
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
WOS000237521300002.pdf1.58 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.