A class of sparsely connected autoassociative morphological memories for large color images.
This brief introduces a new class of sparsely connected autoassociative morphological memories (AMMs) that can be effectively used to process large multivalued patterns, which include color images as a particular case. Such as the single-valued AMMs, the multivalued models exhibit optimal absolute storage capacity and one-step convergence. The remarkable feature of the proposed models is their sparse structure. In fact, the number of synaptic junctions--and consequently the required computational resources--usually decreases considerably as more and more patterns are stored in the novel multivalued AMMs.
Department of Mathematics, State University of Londrina, Londrina PR 86051-990, Brazil. valle@uel.br
Marcos Eduardo
ME
Valle
D000465:Algorithms; D001185:Artificial Intelligence; D001245:Association Learning; D003116:Color; D003198:Computer Simulation; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*