| EPSRC Reference: |
GR/M67490/01 |
| Title: |
A MORPHOLOGICAL AND NEURO-FUZZY BASED IMAGE SEGMENTATION FRAMEWORK FOR MULTIDIMENSIONAL IMAGINGS |
| Principal Investigator: |
Razaz, Dr M |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Computing Sciences |
| Organisation: |
University of East Anglia |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 December 1999 |
Ends: |
30 November 2002 |
Value (£): |
150,022
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
| Manufacturing |
Chemicals |
| Food and Drink |
Pharmaceuticals and Biotechnology |
| Information Technologies |
No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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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.
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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.uea.ac.uk |