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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
Published on: September 3, 2021
Wavelet-based visual compass
Stefan Meyer1, James C Knight1, Alexander Dewar2
1Sussex AI, School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom.
Ants use visual snapshots for navigation. Pre-processing images with Haar wavelets improves route following robustness, even with environmental changes, by highlighting distant objects.
Area of Science:
- Biomimetics
- Neuroscience
- Robotics
Background:
- Ants are exceptional visual navigators, inspiring biomimetic robotics.
- Visual compass-style snapshot models use stored images for route following.
- Haar wavelets pre-processing enhances snapshot robustness.
Purpose of the Study:
- Investigate properties and limits of bearing recovery using visual navigation.
- Compare wavelet-processed, edge-processed, and raw images under perturbations.
- Analyze localized spatial-frequency encodings for route following.
Main Methods:
- Utilized Haar wavelets for image pre-processing.
- Compared image resolutions and processing techniques (wavelets, edge, raw).
- Assessed performance under naturalistic environmental perturbations.
Main Results:
- High-frequency wavelet coefficients highlight distant objects.
- Resolution decrease blurs distant objects, diminishing wavelet effectiveness.
- Environmental perturbations are compensated by appropriate image processing.
Conclusions:
- Localized spatial frequency analysis is crucial for robust snapshot navigation.
- Image processing choice significantly impacts navigation accuracy and resilience.
- Findings advance understanding of ant navigation and robotic applications.
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