Current page: Members->List of Members->David Hogg
 

David Hogg
University of Leeds
Membership Number: 28
Address: School of Computing, Leeds, LS2 9JT, United Kingdom
Email: dch@comp.leeds.ac.uk
Phone: +44 (0)113 3435765
Fax: +44 (0)113 3435468
URL: http://www.comp.leeds.ac.uk/dch/

Biographical Sketch
David Hogg (1954) is Professor of Artificial Intelligence and Pro-Vice-Chancellor at the University of Leeds. He graduated from the University of Warwick with a BSc in Applied Mathematics and from the University of Western Ontario with an MSc in Computer Science, before joining the Plessey company as a Systems Analyst. In 1978 he started his PhD research in computer vision at the University of Sussex, where he went on to become a Lecturer within the School of Cognitive and Computing Sciences in 1984. David Hogg was appointed as a Full Professor at the University of Leeds in 1990.

His research has focused on the development and application of spatio-temporal models within computer vision, dealing especially with 3D models, stochastic processes and automated learning. He is a regular reviewer for the major computer vision journals and has served on the programme committee for the principal international conferences in the field.

Leeds University (School of Computing)
The School of Computing at Leeds University is one of the largest research-based computer science departments in the UK. Within the School, the Computer Vision and Knowledge Representation groups have been in existence for over 10 years and now have a total of nine permanent members of academic staff.

The Computer Vision group has pioneered the development and application of spatio-temporal dynamical models for the analysis of images depicting non-rigid objects such as people and animals. Early work on tracking has developed into a broader interest in behaviour, with application to surveillance, animal husbandry, and human-computer interaction. A key feature of this work is the acquisition of object and event models from video corpora using statistical learning procedures. The overall approach is aimed at producing systems that are robust, scaleable and autonomous - essential requirements in most applications. This work is funded by grants from the EU, UK research councils and industry.

The Knowledge Representation group has developed a variety of calculi for handling qualitative spatial information, including the well known RCC mereotopological language and calculi for handling orientation information and for dealing with vagueness, uncertainty and granularity changes. The group is concerned not only with underlying ontological issues, but also with developing both theoretically complete calculi but also decidable and tractable subsets for efficient reasoning. The group has collaborated with the Computer Vision group in applying qualitative spatial calculi to video interpretation.


Site generated on Friday, 06 January 2006