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
A New Associative Model with Dynamical Synapses
Neural Processing Letters
IEEE Transactions on Computers
3D object recognition based on low frequency response and random feature selection
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
Low frequency response and random feature selection applied to face recognition
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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An associative memory AM is a type of neural network commonly used for recalling output patterns from input patterns that might be altered by noise. Most of these models have several constraints that limit their applicability in complex problems. Recently, in [13] a new AM based on some aspects of human brain was introduced, however the authors only test its accuracy using image patterns. In this paper we show that this model is also robust with other type of patterns such as voice signal patterns. The AM is trained with associations composed by voice signals and their corresponding images. Once trained, when a voice signal is used to stimulate the AM we expect the memory recall the image associated to the voice signal. In order to test the accuracy of the proposal, a benchmark of sounds and images was used.