Abstract
We show how the binary αβ associative memories recently proposed by Yáñez in [1] can be extended to work now in the gray-level case. To get the desired extension we take the operators α and β, foundation of the αβ memories, and propose a more general family of operators among them the original operators α and β are a subset. For this we formulate a set of functional equations, solve this system and find a family of solutions. We show that the α and β originally proposed in [1] are just a particular case of this new family. We give the properties of the new operators. We then use these operators to build the extended memories. We provide the conditions under which the proposed extended memories are able to recall a pattern either from the pattern’s fundamental set or from altered versions of them. We provide real examples with images where the proposed memories show their efficiency.
Chapter PDF
Similar content being viewed by others
References
Yáñez, C.: Associative Memories based on Order Relations and Binary Operators (In Spanish), PhD Thesis, Center for Computing Research (February 2002)
Dhombres, A.: Functional Equations Containing Several Variables. Cambridge University Press, Cambridge (1991)
Aczel, J.: Functional Equations: History, Applications and Theory. Kluwer Academic Pub. Group, Dordrecht (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sossa, H., Barrón, R., Cuevas, F., Aguilar, C., Cortés, H. (2004). Extended Associative Memories for Recalling Gray Level Patterns. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2004. Lecture Notes in Computer Science, vol 3287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30463-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-30463-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23527-9
Online ISBN: 978-3-540-30463-0
eBook Packages: Springer Book Archive