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.

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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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ID Code: 68815
Item Type: Conference Paper
Refereed: Yes
Keywords: Autonomous underwater vehicles, Feature extraction, Geophysical image processing, Image classification
ISBN: 9781457714276
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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