Professor John A Perrone
Qualifications: MSc PhD Cant
John Perrone specialises in teaching and research in the field of vision and visual perception with a particular emphasis on visual motion perception. Before coming to Waikato, John spent time working at NASA Ames Research Centre in the USA, working with vision scientists there to develop computer models of primate visual motion processing and navigation, and where he contributed to one of the first computer models of early motion processing in the primate brain.
Visual perception; illusions; visual aspects of driving or flying; human visual navigation; robot vision systems; computer models of the visual system.
Vision and visual perception, 3-D stereo vision and Virtual Reality. Using computer modelling techniques to simulate the properties of motion sensitive cells in the primate brain. Extracting odometry and depth information from monocular video sequences. Developing biologically-based visual sensors for robotics and autonomous vehicles.
Perrone, J., Cree, M., & Hedayati, M. (2019). Using the properties of Primate Motion Sensitive Neurons to extract camera motion and depth from brief 2-D Monocular Image Sequences. In International Conference on Computer Analysis of Images and Patterns Vol. 11678 (pp. 600-612). Conference held at Salerno, Italy. doi:10.1007/978-3-030-29888-3_49
Hedayati, H., McGuinness, B. J., Cree, M. J., & Perrone, J. A. (2019). Generalization approach for CNN-based object detection in unconstrained outdoor environments. In International Conference Image and Vision Computing New Zealand Vol. 2019-December. doi:10.1109/IVCNZ48456.2019.8960992
Hollands, D. M., Perrone, J. A., & Edwards, T. L. (2019). Visual search behaviour and the advance-key procedure. Poster session presented at the meeting of New Zealand Association for Behaviour Analysis Annual Conference. Christchurch, New Zealand.
Perrone, J. A., Cree, M. J., Hedayati, M., & Corlett, D. (2018). Testing a biologically-based system for extracting depth from brief monocular 2-D video sequences. In 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 6 pages). Conference held in Auckland, New Zealand: IEEE. doi:10.1109/IVCNZ.2018.8634781 Open Access version: https://hdl.handle.net/10289/12324