Interpreting visual information to understand the environment is a challenging problem in computer science. Yet biological human vision seems to process the visual environment effortlessly. This supports the notion that understanding biological vision will help to solve problems in machine vision. However, some of the biggest advances in our understanding of human vision have occurred as a direct result of modern computing techniques. We can only really say we understand a complex system fully when we can recreate or simulate it, test hypotheses on the simulation, and take the simulation to the limits of its validity.

Network Aims

1. To foster communication and joint projects between relevant research groups including those working on biological vision (human and non-human animals) computer vision and machine vision.
2. To establish a series of grand challenges focused around well specified tasks where cross-over studies have a strong potential to provide robust solutions.
3. To foster joint cross-discipline grant applications.
4. To explore mechanisms to improve the utility of joint publications for both partners.
5. To equip individual PhD and post-doctoral scientists to be future leaders of cross-over research projects.
6. To establish a lasting vehicle for supporting cross-over biological and machine vision projects.
7. To increase public engagement with the concept of biologically inspired computer vision.

We invite all academics and relevant industrial practitioners interested in the fostering of human and computer vision research to join the Network and to express an interest in future events.

The Network is funded by the EPSRC and hosted by the Universities of Birmingham and Bath under grants held by Andrew Schofield and Darren Cosker