| EPSRC Reference: |
GR/M28613/01 |
| Title: |
RELATIONAL MODELS FOR RECOGNITION AND LEARNING |
| Principal Investigator: |
Hancock, Professor E |
| Other Investigators: |
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| Department: |
Computer Science |
| Organisation: |
University of York |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 December 1998 |
Ends: |
31 May 2002 |
Value (£): |
154,167
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
| Aerospace, Defence and Marine |
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Summary on Grant Application Form |
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The observation underpinning this proposal is that there is an important niche that needs to be filled in developing practical ways of learning relational models for use in machine vision. The aim here is to capitalise on our previous work on matching relational structures to noisy image data. Basic to this proposal is the observation that there exists both timely and practical potential for building on this methodology to develop a framework for both learning and recognition. The theoretical goal of the work is to develop a more comprehensive information theoretic framework for modelling relational structure. The practical goal is to deploy the new framework to learn perceptual; relationships necessary for complex object recognition tasks in computer vision.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.york.ac.uk |