|
|
Professor of Artificial Intelligence 1999-2000 Visiting Professor at the MIT
Media Lab |
Statistical learning has become central to research on computational intelligence, with important but largely independent developments on perceptual tasks like object recognition and on conceptual reasoning. Typically the former involves clustering and regression over quantitative representations based on real linear spaces, whereas the latter involves symbolic procedures over quantitative representations that emphasise the relationships between objects (e.g. next-to, occurs-before, overlaps, similar-shape).
Our research has been looking for ways to bring these developments together. In the EU-funded CogVis project we explored integration in the context of table-top games, using clustering to learn attribute categories for game-objects (e.g. playing cards) and inductive logic programming (Progol) to learn how to play the game. The key to integration was to form a time series of qualitative descriptions of objects on the table-top at salient times. An unexpected finding was that the emergent rules of the game could be used to refine attribute categories in a top-down fashion.
This work is summarised in:
C. J. Needham, P. E. Santos, D. R. Magee, V. Devin, D. C. Hogg, and A. G. Cohn, Protocols from Perceptual Observations, Artificial Intelligence, 167(1-2), 103-136, 2005.
More details of the project can be found on our local CogVis web page.
I've had a longstanding interest in finding better ways to model the shapes and behaviours of objects within a scene. In early work, we focussed on modelling the projected shapes of moving objects and their trajectories through a scene (see the publications by Adam Baumberg and Neil Johnson). This approach was extended to track the hand in 3D and, using a related approach, to acquire 3D models of moving objects (see the publications by Tony Heap and Shen Xinquan).
More recently, Hannah Dee has been taking a different approach in which she is attempting to explain the movements of people within wide-areas scenes in terms of their intentions. The most recent publication on this work is:
H. Dee and D. Hogg, Detecting inexplicable behaviour, British Machine Vision Conference 2004, Kingston, 2004.
Back in 1998 we developed a way to synthesise an interactive agent through unsupervised learning of a joint model of interactive behaviour (see the CVPR98 paper by Neil Johnson and Aphrodite Galata). This has been developed since then to model a reactive face and talking-head.
Full details of most of these projects and others can be found at the vision group's website.
