Burles, Nathan orcid.org/0000-0003-3030-1675, O'Keefe, Simon orcid.org/0000-0001-5957-2474 and Austin, James orcid.org/0000-0001-5762-8614 (2014) Incorporating scale invariance into the cellular associative neural network. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer , GBR , pp. 435-442.
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.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Keywords: | associative memory,cellular automata,correlation matrix memory,distributed computation,pattern recognition,scale invariance |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York |
Depositing User: | Pure (York) |
Date Deposited: | 15 Jun 2016 09:41 |
Last Modified: | 21 Jan 2025 18:21 |
Published Version: | https://doi.org/10.1007/978-3-319-11179-7_55 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-11179-7_55 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:88232 |
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