Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159291
Title: Bioinspired robotic vision with online learning capability and rotation-invariant properties
Authors: Berco, Dan
Ang, Diing Shenp
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Berco, D. & Ang, D. S. (2021). Bioinspired robotic vision with online learning capability and rotation-invariant properties. Advanced Intelligent Systems, 3(8), 2100025-. https://dx.doi.org/10.1002/aisy.202100025
Project: MOE2016-T2-1-102
MOE2016-T2-2-102
Journal: Advanced Intelligent Systems
Abstract: Reliable image perception is critical for living organisms. Biologic sensory organs and nervous systems evolved interdependently to allow apprehension of visual information regardless of spatial orientation. By contrast, convolutional neural networks usually have limited tolerance to rotational transformations. There are software-based approaches used to address this issue, such as artificial rotation of training data or preliminary image processing. However, these workarounds require a large computational effort and are mostly done offline. This work presents a bioinspired, robotic vision system with inherent rotation-invariant properties that may be taught either offline or in real time by feeding back error indications. It is successfully trained to counter the move of a human player in a game of Paper Scissors Stone. The architecture and operation principles are first discussed alongside the experimental setup. This is followed by performance analysis of pattern recognition under misaligned and rotated conditions. Finally, the process of online, supervised learning is demonstrated and analyzed.
URI: https://hdl.handle.net/10356/159291
ISSN: 2640-4567
DOI: 10.1002/aisy.202100025
Rights: © 2021 The Authors. Advanced Intelligent Systems published by Wiley- VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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