Dr Phil Skelton

Research Fellow

School of Electrical and Mechanical Engineering

College of Engineering and Information Technology

Year	Citation
2026	Finn, A., Younger, J., Skelton, P. S. M., Peters, S., O’Hehir, J., Turner, D., & Lucieer, A. (2026). Extracting Value from Fused Aerial and Terrestrial LiDAR Scans. Remote Sensing, 18(16), 2644-1-2644-37. DOI
2026	Finn, A., Skelton, P. S. M., O’Hehir, J., Schebella, D., Winkley, N., & Jenkin, B. (2026). Predicting Future Forest Plantation Establishment Outcomes from UAV-Derived Pre-Planting Environmental Conditions. Remote Sensing, 18(16), 33 pages. DOI
2024	Pegoli, S. P., Skelton, P. S. M., & Brinkworth, R. S. A. (2024). Optimising electromechanical whisker design for contact localisation. Sensors and Actuators A: Physical, 376, 1-16. DOI Scopus1 WoS1
2022	Skelton, P. S. M., Finn, A., & Brinkworth, R. S. A. (2022). Contrast independent biologically inspired translational optic flow estimation. Biological Cybernetics, 116(5/6), 635-660. DOI Scopus2 WoS1 Europe PMC1
2022	Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022). A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance. Sensors, 22(6, article no. 2181), 1-30. DOI Scopus84 WoS60 Europe PMC6
2022	Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022). An investigation of preprocessing filters and deep learning methods for vessel type classification with underwater acoustic data. IEEE Access, 10, 117582-117596. DOI Scopus41 WoS30
2019	Skelton, P. S. M., Finn, A., & Brinkworth, R. S. A. (2019). Consistent estimation of rotational optical flow in real environments using a biologically-inspired vision algorithm on embedded hardware. Image and vision computing, 92(103814), 1-13. DOI Scopus12 WoS11

Year	Citation
2020	Skelton, P. S. M., Finn, A., & Brinkworth, R. S. A. (2020). Improving an optical flow estimator inspired by insect biology using adaptive genetic algorithms. In IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings (pp. 1-10). US: IEEE. DOI Scopus5 WoS5
2017	Skelton, P. S. M., Finn, A., & Brinkworth, R. S. A. (2017). Real-time visual rotational velocity estimation using a biologically-inspired algorithm on embedded hardware. In 2017 International Conference On Digital Image Computing - Techniques And Applications (Dicta) Vol. 2017-December (pp. 149-156). US: IEEE. DOI Scopus3
2017	Skelton, P. S. M., Finn, A., & Brinkworth, R. S. A. (2017). Real-time visual rotational velocity estimation using a biologically-inspired algorithm on embedded hardware. In Y. Guo, H. Li, W. Cai, M. Murshed, Z. Wang, J. Gao, & D. D. Feng (Eds.), 2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA) (pp. 149-156). AUSTRALIA, Sydney: IEEE. WoS3
2013	Skelton, P. S. M., Creek, A. M., Smith, T. G., Saunders, C., & Lee, S. (2013). Review of autonomous systems and research on autonomy for agricultural vehicles. In 2013 Society for Engineering in Agriculture Conference : innovative agricultural technologies for a sustainable future (pp. 1-9). Barton, ACT: Engineers Australia.

Year	Citation
2020	Brinkworth, R., Finn, A., Griffiths, D., & Skelton, P. S. M. (2020). 2020900764, A data processing method.

Position: Research Fellow
Email: phil.skelton@adelaide.edu.au

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