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Details of Grant 

EPSRC Reference: GR/M67490/01
Title: A MORPHOLOGICAL AND NEURO-FUZZY BASED IMAGE SEGMENTATION FRAMEWORK FOR MULTIDIMENSIONAL IMAGINGS
Principal Investigator: Razaz, Dr M
Other Investigators:
Moore, Professor G Tovey, Dr NK Morris, Dr V
Researcher Co-Investigators:
Project Partners:
Department: Computing Sciences
Organisation: University of East Anglia
Scheme: Standard Research (Pre-FEC)
Starts: 01 December 1999 Ends: 30 November 2002 Value (£): 150,022
EPSRC Research Topic Classifications:
Analytical Science
EPSRC Industrial Sector Classifications:
Manufacturing Chemicals
Food and Drink Pharmaceuticals and Biotechnology
Information Technologies No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
Imaging is essential to a wide variety of disciplines in analytical science and relevant industry. Typical examples are NMR, SEM, SOM, optical microscopy, etc. Such imaging techniques are also of considerable interest to the participating groups in this proposal and are extensively used for a variety of analytical science and other applications. These imaging techniques often produce very large multidimensional raw images of different modalities, which require further processing such as enhancement, visualisation and subsequent image interpretation and analysis. A significant question if the degree to which the interpretation and analysis of such data sets can be automated using image processing techniques. Automatic interpretation and analysis of objects/patterns within such real images remain an important challenge, particularly in an analytical context. A major obstacle to this automation is the lack of availability of efficient, reliable and versatile segmentation algorithms, particularly for 3D images. This is further complicated by the presence of blur and noise in real images. The major aim of this proposal is to address this challenging problem by developing, within a single integrated environment, a new segmentation framework that is robust, flexible, trainable and adaptable using a combination of mathematical morphology, fuzzy logic and neural networks.
Key Findings
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Summary
Date Materialised
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Organisation Website: http://www.uea.ac.uk