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Steven_226

Owheo Building, Room 245
Tel +64 3 479 8501
Email steven.mills@otago.ac.nz

I received my BSc (Hons) and PhD in Computer Science from the University of Otago, and completed my studies 2000. After working for a short time in Christchurch as a software developer I took up a lectureship at The University of Nottingham. In 2006 I returned to New Zealand and worked in commercial research and development at the Geospatial Research Centre and then Areograph Ltd before returning to the Computer Science Department as a lecturer in 2011.

My research interests are in computer vision, and particularly in the reconstruction of 3D scenes from multiple views. While there are a number of outstanding issues, recent advances mean that a wide range of scenes can be reconstructed from images alone. Applications of this technology include terrain modelling from aerial imagery, construction of architectural models, and generation of realistic environments for games and entertainment. I am also interested in related fields such as motion analysis, stereo vision, and image based rendering, as well as applications of computer vision and image processing to the analysis of scientific imagery.

For more information, please see my research pages

Publications

Mills, S., Castro, J., O'Regan, G., Arun, L., & Walter, R. (2024). Fast, good, and cheap in desktop 3D scanning for lithic analysis. Proceedings of the New Zealand Archaeological Association (NZAA) Annual Conference. (pp. 28). Retrieved from https://nzarchaeology.org Conference Contribution - Published proceedings: Abstract

Mills, S., Bennani, H., O'Regan, G., Arun, L., Chakraborty, T., & Walter, R. (2024). Fast, good, and cheap: You can have all three with desktop 3D scanning for lithic analysis. Proceedings of the 51st Computer Applications and Quantitative Methods in Archaeology (CAA) International Conference. 179, (pp. 175). Retrieved from https://2024.caaconference.org/ Conference Contribution - Published proceedings: Abstract

Mills, S., Das, N., O'Regan, G., Arun, L., Chakraborty, T., & Walter, R. (2024). A framework for integrating domain knowledge and deep learning for 3D shape analysis of lithic fragments. Proceedings of the 51st Computer Applications and Quantitative Methods in Archaeology (CAA) International Conference. 172, (pp. 108-109). Retrieved from https://2024.caaconference.org/ Conference Contribution - Published proceedings: Abstract

Liu, J., McCane, B., & Mills, S. (2024). Learning to explore by reinforcement over high-level options. Machine Vision & Applications, 35, 6. doi: 10.1007/s00138-023-01492-1 Journal - Research Article

Baker, L., Ventura, J., Langlotz, T., Gul, S., Mills, S., & Zollmann, S. (2024). Localization and tracking of stationary users for augmented reality. Visual Computer, 40, 227-244. doi: 10.1007/s00371-023-02777-2 Journal - Research Article

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