Morphology neural networks: an introduction with applications
Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
Novel Neural Network Models for Computing HomotheticInvariances: An Image Algebra Notation
Journal of Mathematical Imaging and Vision
Morphological bidirectional associative memories
Neural Networks
Generalizing Operations of Binary Autoassociative Morphological Memories Using Fuzzy Set Theory
Journal of Mathematical Imaging and Vision
Reconstruction of Patterns from Noisy Inputs Using Morphological Associative Memories
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Associative morphological memories based on variations of the kernel and dual kernel methods
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
A Bidirectional Hetero-Associative Memory for True-Color Patterns
Neural Processing Letters
A New Associative Model with Dynamical Synapses
Neural Processing Letters
IEEE Transactions on Computers
Morphological associative memories
IEEE Transactions on Neural Networks
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
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Morphological associative memories (MAMs) are a special type of associative memory which exhibit optimal absolute storage capacity and one-step convergence. This associative model substitutes the additions and multiplications used in the classical models by additions/subtractions and maximums/minimums depending on the proposed model. MAMs have been applied to different pattern recognition problems including face localization and gray scale image restoration. Despite of his power, it has not been applied in problems that involve true-color patterns. In this paper we show how a Morphological Auto-associative Memory (MAAM) can be applied to restore true-color patterns. We present a study of the behavior of this associative model with a benchmark of 14400 images altered by different type of noises.