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Professor John A Perrone

Qualifications: MSc PhD Cant

([email protected])

About John

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.

Expertise

Visual perception; illusions; visual aspects of driving or flying; human visual navigation; robot vision systems; computer models of the visual system.

Research Interests

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.

Recent Publications

  • 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

  • Perrone, J. A. (2018). Visual–vestibular estimation of the body's curvilinear motion through the world: A computational model. Journal of Vision, 18(4), 1-32. doi:10.1167/18.4.1

  • 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

View all research publications by John Perrone

Contact Details

Name  Extn.  Username  Room  Department
Perrone, Prof John 9229 jpnz K.1.08 School of Psychology