Adaptive Fuzzy Morphological Filtering of Impulse Noisein Images
Multidimensional Systems and Signal Processing
A new algorithm for training multi-layered morphological networks
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Information Sciences: an International Journal
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
Hi-index | 0.01 |
We formulate a general class of neural network based filters, where each node is a morphological/rank operation. This type of system is computationally efficient since no multiplications are necessary. The introduction of such networks is partially motivated from observations that internal structures of a neuron can generate logic operations. An efficient adaptive optimal design procedure is proposed for these networks, based on the back-propagation algorithm. The procedure is optimal under the LMS criterion. Finally, experimental results are illustrated in problems of noise cancellation, encouraging the use of such class of systems and its training algorithm as important tools for nonlinear signal and image processing.