Simultaneous underwater visibility assessment, enhancement and improved stereo

Roser, Martin, Dunbabin, Matthew, & Geiger, Andreas (2014) Simultaneous underwater visibility assessment, enhancement and improved stereo. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), IEEE, Hong Kong Convention and Exhibition Center, Hong Kong, China, pp. 3840-3847.

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Vision-based underwater navigation and obstacle avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility. This paper describes a novel method for the simultaneous underwater image quality assessment, visibility enhancement and disparity computation to increase stereo range resolution under dynamic, natural lighting and turbid conditions. The technique estimates the visibility properties from a sparse 3D map of the original degraded image using a physical underwater light attenuation model. Firstly, an iterated distance-adaptive image contrast enhancement enables a dense disparity computation and visibility estimation. Secondly, using a light attenuation model for ocean water, a color corrected stereo underwater image is obtained along with a visibility distance estimate. Experimental results in shallow, naturally lit, high-turbidity coastal environments show the proposed technique improves range estimation over the original images as well as image quality and color for habitat classification. Furthermore, the recursiveness and robustness of the technique allows implementation onboard an Autonomous Underwater Vehicle for improving navigation and obstacle avoidance performance.

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14 citations in Scopus
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ID Code: 81676
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: autonomous underwater vehicle, image enhancement, robot vision, color correction, high-turbidity coastal environments, natural lighting
DOI: 10.1109/ICRA.2014.6907416
ISBN: 9781479936854
Divisions: Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 IEEE
Deposited On: 08 Feb 2015 23:15
Last Modified: 24 Jun 2017 02:03

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