Multi-template training for image processing with cellular neural networks
Research output: Contribution to conferences › Paper › Contributed › peer-review
Contributors
Abstract
Cellular neural networks (CNN) are often considered as massive parallel computing arrays for high speed image processing. In order to find appropriate CNN templates, optimization methods are necessary in many cases. We consider the optimization method Iterative Annealing directly using the output of a hardware realization of a CNN-UM Chip. The procedure presented in this contribution generates highly adapted sets of templates for complex image processing tasks. With this approach it is also possible to tune existing CNN programs to compensate inaccuracies of analog CNN hardware leading to noise reduction and more robust behaviour. Finally, an application of practical interest has been developed, by using the introduced method. We achieved the tracing of a certain selected object out of an image sequence showing many moving objects.
Details
Original language | English |
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Pages | 523-531 |
Number of pages | 9 |
Publication status | Published - 2002 |
Peer-reviewed | Yes |
Externally published | Yes |
Workshop
Title | 7th IEEE International Workshop on Cellular Neural Networks and their Applications |
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Abbreviated title | CNNA 2002 |
Conference number | 7 |
Duration | 22 - 24 July 2002 |
Degree of recognition | International event |
City | Frankfurt am Main |
Country | Germany |
External IDs
ORCID | /0000-0001-7436-0103/work/142240267 |
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Keywords
ASJC Scopus subject areas
Keywords
- Annealing, Cellular neural networks, Hardware, Image processing, Image sequences, Iterative methods, Noise reduction, Noise robustness, Optimization methods, Parallel processing