Standard machine vision approaches are challenged in applications where large amounts of noisy temporal data must be processed in real-time. This work aims to develop neuromorphic event-based processing systems for such challenging, high-noise environments. The novel event-based application-focused algorithms developed are primarily designed for implementation in digital neuromorphic hardware with a focus on noise robustness, ease of implementation, operationally useful ancillary signals and processing speed in embedded systems.
| Date of Award | 2020 |
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| Original language | English |
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- neural networks (computer science)
- neuromorphic engineering
- neuromorphics
- computer vision
- data processing
High speed event-based visual processing in the presence of noise
Afshar, S. (Author). 2020
Western Sydney University thesis: Doctoral thesis