Abstract
Dynamic vision is a subfield of computer vision dealing explicitly with problems characterized by image features that evolve in time according to some underlying dynamics. Examples include sustained target tracking, activity classification from video sequences, and recovering 3D geometry from 2D video data. This article discusses the central role that systems theory can play in developing a robust dynamic vision framework, ultimately leading to vision-based systems with enhanced autonomy, capable of operating in stochastic, cluttered environments.
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Sznaier, M., Camps, O. (2020). Uncertainty and Robustness in Dynamic Vision. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_134-2
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_134-2
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Latest
Uncertainty and Robustness in Dynamic Vision- Published:
- 29 September 2020
DOI: https://doi.org/10.1007/978-1-4471-5102-9_134-2
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Original
Uncertainty and Robustness in Dynamic Vision- Published:
- 12 March 2014
DOI: https://doi.org/10.1007/978-1-4471-5102-9_134-1