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Details of Grant 

EPSRC Reference: GR/L78161/01
Title: PROBABILISTIC MODELS FOR SEQUENCES
Principal Investigator: Williams, Professor C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
BAE Systems QinetiQ
Department: Sch of Informatics
Organisation: University of Edinburgh
Scheme: Standard Research (Pre-FEC)
Starts: 01 October 1998 Ends: 30 September 2001 Value (£): 172,708
EPSRC Research Topic Classifications:
Image & Vision Computing
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
Much of the data that researchers would like to model and understand is generated by multiple independent cause. For example, many separate objects which can be moving independently are involved in road-scene image-sequences. Current hidden-cause models for temporal sequences, notably the hidden Markov model, are not able to exploit efficiently this multiple-cause structure. The aim of this proposal is to develop multiple-cause probabilistic models that can be used for sequential problems, and to exploit them in real-world problems.A three-year work programme is planned for the PI, one post-doctoral RA and a PhD student, with the assistance of a MSc student and four months of research programmer time. In the theoretical part of the project the PI and RA will develop and evaluate approximation and bounding techniques for inference and learning for multiple-cause sequence models. The PhD student will apply these methods for a road-scene analysis problem, and the Msc student will assist the RA in applying the techniques to the segmentation of MRI brain-image sequences. It is expected that the approach will lead to improved prediction accuracy in these tasks. The programmer will upgrade the research code to enhance its transfer to the industrial partners and wider research community.
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