Real-time image classification for adaptive mission planning using an Autonomous Underwater Vehicle
Durrant, Andrew & Dunbabin, Matthew (2011) Real-time image classification for adaptive mission planning using an Autonomous Underwater Vehicle. In Proceedings of OCEANS 2011, IEEE, Kona, Hawaii, pp. 1-6.
Real-time image analysis and classification onboard robotic marine vehicles, such as AUVs, is a key step in the realisation of adaptive mission planning for large-scale habitat mapping in previously unexplored environments. This paper describes a novel technique to train, process, and classify images collected onboard an AUV used in relatively shallow waters with poor visibility and non-uniform lighting. The approach utilises Förstner feature detectors and Laws texture energy masks for image characterisation, and a bag of words approach for feature recognition. To improve classification performance we propose a usefulness gain to learn the importance of each histogram component for each class. Experimental results illustrate the performance of the system in characterisation of a variety of marine habitats and its ability to operate onboard an AUV's main processor suitable for real-time mission planning.
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|Item Type:||Conference Paper|
|Keywords:||Autonomous underwater vehicles, Feature extraction, Geophysical image processing, Image classification|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 IEEE|
|Deposited On:||19 Mar 2014 23:39|
|Last Modified:||02 Apr 2014 23:37|
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