Summary
Reliable and detailed underwater scene sensing, and analysis has a fundamental role in numerous underwater activities drawing both scientific and economic interest. Computer vision and remote sensing have evolved impressively in the last decade, propelled also by advancements in deep learning, enabling unprecedented levels of robust and automatic information extraction from visual data. At the same time, there is increasing interest in visual based underwater sensing, however, deep learning methods are less explored in this domain. On the other hand, sensing modalities based on Single Photon Cameras (SPCs) and transient imaging are gradually maturing, having certain characteristics that allow sensing in challenging visibility conditions, as the ones of underwater environments.
iSEAu aims to significantly advance the state-of-the-art of underwater scene sensing by bridging the gap in the use of data-driven methods in underwater perception, and by combining the respective advantages of SPCs, multispectral and conventional cameras. Investing on intensive knowledge transfer, the goal is to bring together the fields of computer vision, machine learning and remote sensing for optimally addressing the underwater visual sensing challenges. The project objectives address these challenges in two levels. The first concerns the development of methods for “removing the water” from underwater images by harnessing the power of learning-based methods, and the development of methods based on SPC transient imaging for perception in challenging visibility conditions. The second level concerns the adaptation and enhancement to the underwater domain of state-of-the-art methods for image-based extraction of structural and semantic information, and their field-testing considering representative application scenarios. In summary, iSEAu will provide novel data-driven methodologies and technological solutions to researchers, scientists and users for underwater sensing of unmatched fidelity.
iSEAu aims to significantly advance the state-of-the-art of underwater scene sensing by bridging the gap in the use of data-driven methods in underwater perception, and by combining the respective advantages of SPCs, multispectral and conventional cameras. Investing on intensive knowledge transfer, the goal is to bring together the fields of computer vision, machine learning and remote sensing for optimally addressing the underwater visual sensing challenges. The project objectives address these challenges in two levels. The first concerns the development of methods for “removing the water” from underwater images by harnessing the power of learning-based methods, and the development of methods based on SPC transient imaging for perception in challenging visibility conditions. The second level concerns the adaptation and enhancement to the underwater domain of state-of-the-art methods for image-based extraction of structural and semantic information, and their field-testing considering representative application scenarios. In summary, iSEAu will provide novel data-driven methodologies and technological solutions to researchers, scientists and users for underwater sensing of unmatched fidelity.
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| Web resources: | https://cordis.europa.eu/project/id/101030367 |
| Start date: | 01-03-2022 |
| End date: | 30-12-2024 |
| Total budget - Public funding: | 165 085,44 Euro - 165 085,00 Euro |
Cordis data
Original description
Reliable and detailed underwater scene sensing, and analysis has a fundamental role in numerous underwater activities drawing both scientific and economic interest. Computer vision and remote sensing have evolved impressively in the last decade, propelled also by advancements in deep learning, enabling unprecedented levels of robust and automatic information extraction from visual data. At the same time, there is increasing interest in visual based underwater sensing, however, deep learning methods are less explored in this domain. On the other hand, sensing modalities based on Single Photon Cameras (SPCs) and transient imaging are gradually maturing, having certain characteristics that allow sensing in challenging visibility conditions, as the ones of underwater environments.iSEAu aims to significantly advance the state-of-the-art of underwater scene sensing by bridging the gap in the use of data-driven methods in underwater perception, and by combining the respective advantages of SPCs, multispectral and conventional cameras. Investing on intensive knowledge transfer, the goal is to bring together the fields of computer vision, machine learning and remote sensing for optimally addressing the underwater visual sensing challenges. The project objectives address these challenges in two levels. The first concerns the development of methods for “removing the water” from underwater images by harnessing the power of learning-based methods, and the development of methods based on SPC transient imaging for perception in challenging visibility conditions. The second level concerns the adaptation and enhancement to the underwater domain of state-of-the-art methods for image-based extraction of structural and semantic information, and their field-testing considering representative application scenarios. In summary, iSEAu will provide novel data-driven methodologies and technological solutions to researchers, scientists and users for underwater sensing of unmatched fidelity.
Status
SIGNEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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