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.