| EPSRC Reference: |
EP/Y002490/1 |
| Title: |
MyUnderwaterWorld: Intelligent Underwater Scene Representation |
| Principal Investigator: |
Anantrasirichai, Dr N |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Department: |
Computer Science |
| Organisation: |
University of Bristol |
| Scheme: |
Standard Research - NR1 |
| Starts: |
12 February 2024 |
Ends: |
11 August 2025 |
Value (£): |
164,681
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| EPSRC Research Topic Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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17 May 2023
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ECR International Collaboration Grants Panel 1
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Announced
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Summary on Grant Application Form |
The oceans have been explored for hundreds of years and the activities still continue, but they are always limited by the number of diving experts, technologies and in particular costs. Advanced imaging enables transferable underwater discovery to onshore experts with specific knowledge required, such as geologists, archaeologists and biologists. Three-dimensional (3D) reconstruction from these image sequences enhance understanding of underwater organisms, objects and the seabed. However, current solutions have not yet provided high resolution and definition of underwater 3D representation without months of intense enhancements and processing time. This is mainly because of limitation of data and computational complexity as, obviously, processing the sequences of underwater environments is challenging due to distortion, backscatter of light and turbidity conditions.
MyUnderwaterWorld project aims to provide intensive analysis of underwater imagery for Artificial Intelligence (AI)-based development, leading to a novel framework for image quality enhancement and high-resolution 3D scene representation of underwater scene, which contains seabed and objects of interest. We hypothesise that the 3D scene could be modelled accurately and directly from raw underwater data using well-defined prior knowledge. This could be achieved by characterising diverse and reliable underwater datasets. We will combine real-time visual SLAM and sparse radiance fields hierarchically, trained with a novel loss function developed from prior knowledge of underwater. This will improve quality of 3D representation, and offer more efficient and flexible workflows. It will also facilitate more robust feature extraction for subsequent machine-based processing and more efficient compression for delivery.
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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.bris.ac.uk |