Abstract
The use of associative techniques to perform shape recognition is considered. With respect to classical recognition techniques, it is shown how the pattern-completion capability and content addressability of associative memories can yield robust performance and high computational speed in shape recognition applications, especially in those domains in which promptness of ‘first-glance’ classification is more important than accurate pattern-analysis capabilities. The noise-like coding model of associative memory is adopted, specifying the basic associative classification principle. A set of different experimental domains are considered, at increasing levels of complexity, evidencing how the theoretical model can be effectively applied to image and shape recognition.
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© 1992 Springer Science+Business Media New York
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Parodi, G., Vernazza, G., Zunino, R. (1992). Shape Classification by Using Associative Memories. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-0715-8_41
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DOI: https://doi.org/10.1007/978-1-4899-0715-8_41
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