Dr Lee Streeter
Qualifications: PhD Waikato, MSc Physics Waikato
Time-of-flight range image metrology. Image processing. Numerical methods.
Dr Streeter's research interests focus on and about time-of-flight range imaging. His current project is on solving the 'problem of motion', to correct motion blur and transform it into simultaneous distance and motion measurement. He is interested in time-of-flight in general, and has worked on all majors form of error in time-of-flight range imaging.
2019 Lee was awarded the Royal Society of New Zealand Cooper Award.
2019 Lee won the Kudos Datamars Engineering Science Excellence Award.
Lee received congratulations in the NZ parliament: https://www.parliament.nz/resource/en-NZ/OrderPaper_20190918/564ca5a3e81430fad0d3e287b7bae7afd5f4823f
2017 he was awarded the MBIE Smart Ideas to research the implementation of motion measurement and correction in time-of-flight for industrial applications. His work was highlighted at the 2018 Fieldays
2015 Lee was awarded the Marsden Fast Start to research the problem of motion in time-of-flight range imaging.
Find out more about Lee's Marsden grant awarded for his time-of-flight photography research: http://bit.ly/1MvrDu0
I am actively looking for potential PhD students. If you have a good GPA, and possibly have one or more first author conference publications, I may be interested in hearing from you.
I am interested in all areas of computer vision, but especially with a focus on measurement and computational imaging (inverse problems in imaging). Some potential projects follow.
- Ghost Imaging/Ghost Range Imaging.
I am interested in exploring the fascinating topic of ghost imaging, especially with focus on range imaging by ghost imaging. Ghost imaging allows one to measure an image from light that has never interacted with the scene. Instead the correlation between light that has interacted with the scene, captured using a single pixel detector, and light captured by the image sensor direct from the light source, revels the image "ghost".
- Machine Learning for Time-of-Flight Range Imaging.
Machine learning is a hot topic today. Training computers to make decisions based on data is seeing wide uptake across many fields and industries. This open ended research problem is to apply current trends in machine learning to optimise time-of-flight range measurement.
- The Systems Engineering of Error Correction in Time-of-Flight Range Imaging.
The UoW range imaging group leads the way internationally on understanding and improving time-of-flight range imaging. We have developed solutions to all the major error sources. However, an outstanding question is how to bring the existing solutions all together. Many appear incompatible with each other, and a significant research question is how to bring them together, solving multiple error sources at the same time.
- Noise and Precision Optimisation in Time-of-Flight Range Imaging.
Time-of-Flight cameras, like any electronic device, have random error. This random error means that an object at, say, 2 metres from the camera might be measured slightly closer or further from the camera, and that error is unpredictable causing uncertainty. Mitigation of this error is key to producing the best possible range measurements. The research question is to draw from statistics and other sources of knowledge to best understand how random effects cause uncertainty and how to reduce their impact.
- Motion Measurement/Velocimetry.
- Applications of Depth Imaging.
Depth cameras provide rich information of objects within their field of view. The sky is the limit for new applications of depth imaging. Engineering solutions to problems might include behaviour analysis, human computer interface, security applications, health applications (ToF cameras operate in the infrared which can see into and beneath skin), tomographic imaging, etc.
Anthonys, Gehan (submitted).
Corlett, Dale (submitted).
Streeter, L., & Wise, J. J. (2020). Exploring machine learning to reduce motion error in time-of-flight range imaging. In OSA Imaging and Applied Optics Congress. Online (originally to be held in Vancouver, Canada).
Wilson, M. T., Seshadri, S., Streeter, L. V., & Scott, J. B. (2020). Teaching physics concepts without much mathematics: ensuring physics is available to students of all backgrounds. Australasian Journal of Engineering Education, 16 pages. doi:10.1080/22054952.2020.1776027 Open Access version: https://hdl.handle.net/10289/13649
Hébert-Losier, K., Hanzlíková, I., Zheng, C., Streeter, L., & Mayo, M. (2020). The 'DEEP' landing error scoring system. Applied Sciences (Switzerland), 10(3). doi:10.3390/app10030892
Lickfold, C. A., Streeter, L., Cree, M. J., & Scott, J. B. (2019). Frequency Based Radial Velocity Estimation in Time-of-Flight Range Imaging. In 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi:10.1109/ivcnz48456.2019.8961021
Contact DetailsEmail: lee dot streeter at waikato.ac.nz
Phone: +64 7 838 4106