Learning in the presence of concept drift and hidden contexts
Machine Learning
Robust Real-Time Face Detection
International Journal of Computer Vision
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Bayesian data fusion of multiview synthetic aperture sonar iagery for seabed classification
IEEE Transactions on Image Processing
Surveying noctural cuttlefish camouflage behaviour using an AUV
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Path Planning for Autonomous Underwater Vehicles
IEEE Transactions on Robotics
The fusion of large scale classified side-scan sonar image mosaics
IEEE Transactions on Image Processing
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A new adaptive strategy for performing data collection with a sonar-equipped autonomous underwater vehicle (AUV) is proposed. The approach is general in the sense that it is applicable to a wide range of underwater tasks that rely on subsequent processing of side-looking sonar imagery. By intelligently allocating resources and immediately reacting to the data collected in-mission, the proposed approach simultaneously maximizes the information content in the data and decreases overall survey time. These improvements are achieved by adapting the AUV route to prevent portions of the mission area from being either characterized by poor image quality or obscured by shadows caused by sand ripples. The peak correlation of consecutive sonar returns is used as a measure for image quality. To detect the presence of and estimate the orientation of sand ripples, a new innovative algorithm is developed. The components of the overall data-driven path-planning algorithm are purposely constructed to permit fast real-time execution with only minimal AUV onboard processing capabilities. Experimental results based on real sonar data collected at sea are used to demonstrate the promise of the proposed approach.