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Understanding
Participants
Summary
We are interested in how to solve real world practical problems using computer vision with both classical algorithms and machine learning. Environmental monitoring is a particular focus of our work and we have developed a variety systems for image understanding for underwater image classification [1] and for fish monitoring [2]. As well as the applications we are interested in developing the underpinning theory [3].
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Partners
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Publications
[1] . Game and G.D. Finlayson. "Machine learning for non-experts: A more accessible and simpler approach to automatic benthic habitat classification", Ecological Informatics 81, 102619, 2024.
[2] G. French et al. "Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards",ICES Journal of Marine Science 77 (4), 1340-1353, 2020.
[3] G. French, M. Mackiewicz and M. Fisher. " Self-ensembling for visual domain adaptation", International Conference on Learning Representations, 2018.
Geometric Understanding
Participants
Summary
Our research also advances semantic understanding in Geometry Processing [1,2] topics such as 3D reconstruction and mesh generation. Dr Wang currently leads a digital heritage project in collaboration with the National Trust [3] and a Royal Society-supported project focused on urban-scale 3D reconstruction, understanding, and generation for digital twin applications.
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Funding
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Partners
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Publications
[1]C. Jiang, C. Wang et al. "Quad-mesh based isometric mappings and developable surfaces", ACM Transactions on Graphics, SIGGRPH, 2020.
[2] Wang, C., Jiang, C., Tellier, X., Wallner, J. & Pottmann, H., "Planar panels and planar supporting beams in architectural structures", ACM Transactions on Graphics, 2022
[3] C. Wang et al. "Level-of-Detail Digitization of High Ceilings in Virtual Reality", Digital Heritage, 2025