By the same authors

Incorporating scale invariance into the cellular associative neural network

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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Department/unit(s)

Publication details

Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DatePublished - 1 Jan 2014
Pages435-442
Number of pages8
PublisherSpringer-Verlag
Volume8681 LNCS
Original languageEnglish
ISBN (Print)9783319111780

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8681 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Abstract

This paper describes an improvement to the Cellular Associative Neural Network, an architecture based on the distributed model of a cellular automaton, allowing it to perform scale invariant pattern matching. The use of tensor products and superposition of patterns allows the system to recall patterns at multiple resolutions simultaneously. Our experimental results show that the architecture is capable of scale invariant pattern matching, but that further investigation is needed to reduce the distortion introduced by image scaling.

    Research areas

  • associative memory, cellular automata, correlation matrix memory, distributed computation, pattern recognition, scale invariance

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