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An eight layer cellular neural network for spatio‐temporal image filtering
Author(s) -
Shi Bertram E.
Publication year - 2006
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.347
Subject(s) - computer science , neuromorphic engineering , artificial neural network , layer (electronics) , image (mathematics) , artificial intelligence , cellular neural network , electronic circuit , pattern recognition (psychology) , algorithm , engineering , chemistry , organic chemistry , electrical engineering
Spatio‐temporal filters are critical components of biologically inspired or neuromorphic algorithms for image motion analysis. In this paper, we describe eight layer cellular neural network architectures that can be used to implement these filters. Despite the apparently large number of layers, we describe how these architectures can be implemented efficiently using weak inversion transistor circuits. Integrating both spatial and temporal filtering into a single network reduces hardware complexity in comparison with an architecture that cascades separate spatial and temporal filtering stages. In addition, by considering spatial and temporal filtering jointly, we can obtain filters with enhanced velocity selectivity, as well as more robust population responses to moving image input. Copyright © 2006 John Wiley & Sons, Ltd.