Share Email Print

Proceedings Paper

Robust image retrieval from noisy inputs using lattice associative memories
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Lattice associative memories also known as morphological associative memories are fully connected feedforward neural networks with no hidden layers, whose computation at each node is carried out with lattice algebra operations. These networks are a relatively recent development in the field of associative memories that has proven to be an alternative way to work with sets of pattern pairs for which the storage and retrieval stages use minimax algebra. Different associative memory models have been proposed to cope with the problem of pattern recall under input degradations, such as occlusions or random noise, where input patterns can be composed of binary or real valued entries. In comparison to these and other artificial neural network memories, lattice algebra based memories display better performance for storage and recall capability; however, the computational techniques devised to achieve that purpose require additional processing or provide partial success when inputs are presented with undetermined noise levels. Robust retrieval capability of an associative memory model is usually expressed by a high percentage of perfect recalls from non-perfect input. The procedure described here uses noise masking defined by simple lattice operations together with appropriate metrics, such as the normalized mean squared error or signal to noise ratio, to boost the recall performance of either the min or max lattice auto-associative memories. Using a single lattice associative memory, illustrative examples are given that demonstrate the enhanced retrieval of correct gray-scale image associations from inputs corrupted with random noise.

Paper Details

Date Published: 11 February 2009
PDF: 12 pages
Proc. SPIE 7245, Image Processing: Algorithms and Systems VII, 724517 (11 February 2009); doi: 10.1117/12.806886
Show Author Affiliations
Gonzalo Urcid, INAOE (Mexico)
José Angel Nieves-V., INAOE (Mexico)
Anmi García-A., INAOE (Mexico)
Juan Carlos Valdiviezo-N., INAOE (Mexico)

Published in SPIE Proceedings Vol. 7245:
Image Processing: Algorithms and Systems VII
Nasser M. Nasrabadi; Syed A. Rizvi; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

© SPIE. Terms of Use
Back to Top