Real-time image classification for adaptive mission planning using an Autonomous Underwater Vehicle


Autoria(s): Durrant, Andrew; Dunbabin, Matthew
Data(s)

2011

Resumo

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.

Identificador

http://eprints.qut.edu.au/68815/

Publicador

IEEE

Relação

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6107298

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.

Direitos

Copyright 2012 IEEE

Fonte

School of Electrical Engineering & Computer Science; Science & Engineering Faculty

Palavras-Chave #Autonomous underwater vehicles #Feature extraction #Geophysical image processing #Image classification
Tipo

Conference Paper