Current and Past PhD students

Current PhD students

Milan Verma computer generated aesthetics

Bushra Aktar Use of Image Analysis Techniques to extract Quantifiable data from Biological Images joint with Martin Knight in Medical Engineering

Kevin Shan facial expression and body language recognition joint with Sean Gong in Vision

Fatos Berisha image driven animation joint supervision with Alan Johnston UCL Vision Research Lab & CoMPLEX

Andy Rider motion perception joint supervision with Alan Johnston UCL Vision Research Lab & CoMPLEX

Past PhD students

Andrew Anderson. Evolution of sensorymotor neural networks to perform active motion camouflage

 PhD Awarded 2003, now a Research Fellow at University College London

Keith Anderson. Real Time Image Recognition Using Biologically Inspired Algorithms

 PHd awarded 2004 now working in IT sector

Adam Sherwood. Biologically plausable models for spatial orientation

 PhD awarded 2005 now working in City Finance

Possible PhD projects

Modelling colour constancy in bees

When the illumination changes, for example from midday to afternoon, the light reflected from objects changes too - nevertheless we are able to see object colours as constant. This complex ability might be fundamental to animals survival: primates need to reliably identify fruits, bees need to identify the most rewarding flowers in all illumination conditions. Such reliable object identification is also a fundamental challenge in computer vision. But what neural/perceptual mechanisms might be most useful to solve this task under natural conditions? This project will investigate the benefits of various colour constancy mechanisms in bees foraging from flowers. The postgraduate student can draw on a large database of flower colours (reflectance spectra) co-occuring in natural habitats. He or she will use these data for multi-agent based simulations, in which bees will be modelled foraging among flowers distributed in a virtual space. Modelled bees will be equipped with a variety of colour constancy mechanisms to identify the most suitable neural circuitry to cope with biological realistic solution. The candidate will be trained in state-of-the-art modelling of biological systems as well as the biology of colour vision and its use in the economy of nature. The project will be co-supervised by Lars Chittka, in the Biology Department who is an expert in bee vision.

 Computer Generated Aesthetics

The human eye is attracted to beauty. When studying the world around us our brains have the ability to extract information on the aesthetics of what we observe, for example a pretty face stands out in a crowd. Aesthetics is a multifaceted concept; elements by which human observers assess aesthetics in an observed scene include low-level visual measures of symmetry, spatial proportions such as the golden ratio, and global measures such as overall composition. In this project we will develop biologically plausible mathematical models for aesthetic appreciation and test these models predictions against subject performance. This interdisciplinary approach combining philosophy, psychology and computer science allows us to develop a better model of the perceptual process and to invent computer artefacts with a sense of aesthetics for example to assist in automated product design.

If you are interested in possible PhD study with me please send an email to pmco@dcs.qmul.ac.uk