Biologically-Inspired Motion Encoding for Robust Global Motion Estimation.
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Publication Date
2017-03Journal Title
IEEE Trans Image Process
ISSN
1057-7149
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
26
Issue
3
Pages
1521-1535
Language
eng
Type
Article
This Version
AM
Metadata
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Sariyanidi, E., Gunes, H., & Cavallaro, A. (2017). Biologically-Inspired Motion Encoding for Robust Global Motion Estimation.. IEEE Trans Image Process, 26 (3), 1521-1535. https://doi.org/10.1109/TIP.2017.2651394
Abstract
The growing use of cameras embedded in autonomous robotic platforms and worn by people is increasing the importance of accurate global motion estimation (GME). However, existing GME methods may degrade considerably under illumination variations. In this paper, we address this problem by proposing a biologically-inspired GME method that achieves high estimation accuracy in the presence of illumination variations. We mimic the early layers of the human visual cortex with the spatio-temporal Gabor motion energy by adopting the pioneering model of Adelson and Bergen and we provide the closed-form expressions that enable the study and adaptation of this model to different application needs. Moreover, we propose a normalisation scheme for motion energy to tackle temporal illumination variations. Finally, we provide an overall GME scheme which, to the best of our knowledge, achieves the highest accuracy on the Pose, Illumination, and Expression (PIE) database.
Identifiers
External DOI: https://doi.org/10.1109/TIP.2017.2651394
This record's URL: https://www.repository.cam.ac.uk/handle/1810/274130
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