SciELO - Scientific Electronic Library Online

vol.56 issue2Análisis cuantitativo de interacciones moleculares proteína-proteína mediante la combinación de microarreglos y un lector óptico basado en el fenómeno de resonancia de plasmones superficialesAnisotropic cosmology in Sáez-Ballester theory: classical and quantum solutions author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand




Related links

  • Have no similar articlesSimilars in SciELO


Revista mexicana de física

Print version ISSN 0035-001X


CRUZ, B.; BARRON, R.  and  SOSSA, H.. Geometric associative memories applied to pattern restoration. Rev. mex. fis. [online]. 2010, vol.56, n.2, pp.155-165. ISSN 0035-001X.

Two main research areas in Pattern Recognition are pattern classification and pattern restoration. In the literature, many models have been developed to solve many of the problems related to these areas. Among these models, Associative Memories (AMs) can be highlighted. An AM can be seen as a one-layer Neural Network. Recently, a Geometric Algebra based AM model was developed for pattern classification, the so-called Geometric Associative Memories (GAMs). In general, AMs are very efficient for restoring patterns affected BY either additive or subtractive noise, but in the case of mixed noise their efficiency is very poor. In this work, modified GAMs are used to solve the problem of pattern restoration. This new modification makes use of Conformal Geometric Algebra principles and optimization techniques to completely and directly restore patterns affected by (mixed) noise. Numerical and real examples are presented to test whether the modification can be efficiently used for pattern restoration. The proposal is compared with other reported approaches in the literature. Formal conditions are also given to ensure the correct functioning of the proposal.

Keywords : Associative memories; pattern restoration; mixed noise; conformal geometric algebra.

        · abstract in Spanish     · text in English     · English ( pdf )


Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License