Effectiveness of the Block Splitting Approach on Morphological Associative Memory without a Kernel Image

In this paper, a new morphological associative memory (MAM) is presented. The proposed model is based on the morphological associative memory without a kernel image. Although the model is susceptibility to noise in input patterns, the model is useful for practical applications. We try to improve the problem by introducing a block splitting approach to the recalling process. Furthermore, the proposed method also has a merit the size of memory matrices is small. We confirm the effectiveness of the proposed model by autoassociation of alphabet patterns to compare traditional approaches in terms of the noise tolerance and the size of memory matrices.