A multi-aperture optical flow estimation method for an artificial compound eye

Wu, Sidong; Zhang, Gexiang; Neri, Ferrante; Zhu, Ming; Jiang, Tao; Kuhnert, Klaus Dieter

Abstract

© 2019 IOS Press and the authors. All rights reserved. An artificial compound eye (ACE) is a bio-inspired vision sensor which mimics a natural compound eye (typical of insects). This artificial eye is able to visualize large fields of the outside world through multi-aperture. Due to its functioning, the ACE is subject to optical flow, that is an apparent motion of the object visualized by the eye. This paper proposes a method to estimate the optical flow based on capturing multiple images (multi-aperture). In this method, based on descriptors-based initial optical flows, a unified global energy function is presented to incorporate the information of multi-aperture and simultaneously recover the optical flows of multi-aperture. The energy function imposes a compound flow fields consistency assumption along with the brightness constancy and piecewise smoothness assumptions. This formula efficiently binds the flow field in time and space, and further enables view-consistent optical flow estimation. Experimental results on real and synthetic data demonstrate that the proposed method recovers view-consistent optical flows crossed multi-aperture and performs better than other optical flow methods on the multi-aperture images.

Citation

Wu, S., Zhang, G., Neri, F., Zhu, M., Jiang, T., & Kuhnert, K. D. (2019). A multi-aperture optical flow estimation method for an artificial compound eye. Integrated Computer-Aided Engineering, 26(2), 139-157. https://doi.org/10.3233/ICA-180593

• Journal Article Type: Article
• Acceptance Date: Oct 11, 2018
• Online Publication Date: Feb 25, 2019
• Publication Date: Feb 25, 2019
• Deposit Date: Mar 31, 2020
• Publicly Available Date: Apr 1, 2020
• Journal: Integrated Computer-Aided Engineering
• Print ISSN: 1069-2509
• Electronic ISSN: 1875-8835
• Publisher: IOS Press
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 2
• Pages: 139-157
• DOI: https://doi.org/10.3233/ICA-180593
• Public URL: https://nottingham-repository.worktribe.com/output/3705337
• Publisher URL: https://content.iospress.com/articles/integrated-computer-aided-engineering/ica180593