This area combines experimental psychology with advanced data analysis, computer simulation, and mathematical modeling. On the basis of well-defined theoretical concepts, computational approaches can provide a unifying language and methodology that can be used across disciplines ranging from neurobiology to cognitive science, systems biology, and information technology. Research in this area focuses on the computational basis of vision in particular. A central interest concerns computer vision and the computational modelling of vision using neurobiological principles. Cutting-edge research in this area has thus far examined dynamic modelling of human faces, with applications in object tracking and people detection (such as detection of abnormal behaviour), human gesture recognition, computer modelling of colour vision in insects, the development of intelligent imaging systems, semantic modelling of human movement, and the development of spatial vision models.
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- Professor Lars Chittka
- Dr. Chrisantha Fernando
- Professor Shaogang Gong
- Dr Heiko Grossmann
- Professor Peter McOwan
- Dr Marcus Pearce
- Dr Peter Skorupski
- Professor Geraint Wiggins
Selected publications in this research area
Hospedales, T., Gong, S., and Xiang, T. (2012). Video Behaviour Mining Using a Dynamic Topic Model. International Journal of Computer Vision, 98, 303-323.
Loy, C. C., Xiang, T. and Gong, S. (2012). Incremental Activity Modelling in Multiple Disjoint Cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1799-1813.
Fernando, C. (2011). Symbol manipulation and rule learning in spiking neuronal networks. Journal of Theoretical Biology, 25, 29-41.
Fernando, C., Vasas, V., Szathmary, E., et al. (2011). Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain. PLoS ONE, 6, e23534.
Arnold, S.E.J., Faruq, S., Savolainen, V., McOwan, P.W., Chittka, L. (2010). FReD: The Floral Reflectance Database — A Web Portal for Analyses of Flower Colour. PLoS ONE, 5, 0014287.
Nadal, M., and Pearce, M.T. (2010). The Copenhagen neuroaesthetics conference: Prospects and pitfalls for an emerging field. Brain and Cognition, 76, 172–183.
Pearce, M.T., M¨ullensiefen, D., and Wiggins, G.A. (2010). The role of expectation and probabilistic learning in auditory boundary perception: A model comparison. Perception , 39, 1367–1391.
Pearce, M.T., Ruiz, M.H., Kapasi, S., Wiggins, G.A., and Bhattacharya, J. (2010). Unsupervised statistical learning underpins computational, behavioural and neural manifestations of musical expectation. NeuroImage, 50, 302–313.
Verma, M.A., and McOwan, P.W. (2009). Generating customised experimental stimuli for visual search using Genetic Algorithms shows evidence for a continuum of search efficiency. Vision Research, 49, 374-382.
Zhang, J., and Gong, S. (2009). People detection in low-resolution video with non-stationary background. Image and Vision Computing, 27, 437-443.
Liang, M., van Leeuwen, T.M., and Proulx, M.J. (2008). Propagation of brain activity during audiovisual integration. Journal of Neuroscience, 28, 8861-8862.
Shan, S., Gong, S., and McOwan, P. (2008). Fusing gait and face cues for human gender recognition. Neurocomputing, 71, 1931-1938.
Grasshoff, U., Grossmann, H., Holling, H., and Schwabe, R. (2007). Design optimality in multi-factor generalized linear models in the presence of an unrestricted quantitative factor. Journal of Statistical Planning and Inference, 137, 3882-3893.
Grossmann, H., Holling, H., Grasshoff, U., and Schwabe, R. (2006). Optimal designs for asymmetric linear paired comparisons with a profile strength constraint. Metrika, 64, 109-119.
Chittka, L., and Brockmann, A. (2005). Perception space, the final frontier. PLoS Biology, 3, 564-568.
Lotto, R.B., and Chittka, L. (2005). Seeing the light: Illumination as a contextual cue to color choice behavior in bumblebees. Proceedings of the National Academy of Sciences of the United States of America, 102, 3852-3856.
Anderson, A.J., and McOwan, P.W. (2003). Model of a predatory stealth behaviour camouflaging motion. Proceedings of the Royal Society B, 270, 489-495.
Chittka, L., Dyer, A.G., Bock, F., and Dornhaus, A. (2003). Psychophysics: Bees trade off foraging speed for accuracy. Nature, 424, 388-388.
Johnston, A., McOwan, P.W., and Benton, C. (2003). Biological computation of image motion from flows over boundaries. Journal of Physiology, 97, 325-334.