Affiliation
Univ ArizonaIssue Date
2020-08-20Keywords
Event-Based SensorImage Science
Performance
Systems Engineering
Modeling and Simulation
Analysis
Metadata
Show full item recordPublisher
SPIE-INT SOC OPTICAL ENGINEERINGCitation
Cox, J., Ashok, A., & Morley, N. (2020, August). An analysis framework for event-based sensor performance. In Unconventional Imaging and Adaptive Optics 2020 (Vol. 11508, p. 115080R). International Society for Optics and Photonics.Rights
© 2020 SPIE.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Event-Based Sensors (EBSs) are passive electro-optical (EO) imaging sensors which have read-out hardware that only outputs when and where temporal changes in scene brightness are detected. In the case of a static background and platform, this hardware ideally implements background clutter cancellation, leaving only moving object data to be read out. This data reduction leads to a bandwidth reduction, which is equivalent to increasing spatio-temporal resolution. This advantage can be exploited in multiple ways, using trade-offs between spatial and temporal resolution, and between spatial resolution and field-of-view. In this paper, we introduce the EBS concept and our previous experiments and analysis. We discuss important EBS properties, followed by discussion of applications where the EBS could provide significant benefit over conventional frame-based EO sensors. Finally, we present a method for analyzing EBS technology for specific applications (i.e. determine performance compared to conventional technology). This approach involves abstraction of EBS and conventional imaging technology and provides a way to determine the value of EBSs over conventional imaging technology for facilitating future EBS application development.ISSN
0277-786XVersion
Final published versionae974a485f413a2113503eed53cd6c53
10.1117/12.2567620