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Title: Cascade of boosted classifiers for automatic target recognition in synthetic aperture sonar imagery
Author: Jamil Sawas, Yvan Petillot
Year: 2012
Book / Journal: ECUA 2012, 11th European Conference on Underwater Acoustics, Edinburgh, United Kingdom
Abstract: Automatic detection of underwater objects is a critical task for a variety of underwater applications. Rapid detection approaches are needed to tackle the large amount of data produced using state-of-the-art sensors such as Synthetic Aperture Sonar. Accurate detection approaches are also required to reduce the number of false alarms and enable on the fly adaptation of the missions in Autonomous Underwater Vehicles. In this paper we propose a new method for object detection in Synthetic Aperture Sonar imagery capable of processing images extremely rapidly based on the Viola and Jones cascade of boosted classifiers. Our approach provides confidence-rated predictions rather than the {-1,1} of the traditional cascade. This does not only provide a confidence level to each prediction but also reduces the false alarm rate significantly. We also introduce a novel structure of the cascade capable of obtaining low false alarm rates while achieving high detection accuracy. Results obtained on a real dataset of Synthetic Aperture Sonar on a variety of challenging terrains are presented to show the discriminative power of such an approach.
Keywords: Automatic Target Recognition, Underwater Object Detection, Cascade Classifier, Synthetic Aperture Sonar,
BibTex: @conference{Sawas2012a,
author = {Jamil Sawas and Yvan Petillot},
title = {Cascade of boosted classifiers for automatic target recognition in synthetic aperture sonar imagery},
year = {2012},
journal = {11th European Conference on Underwater Acoustics},
location = {Edinburgh, Scotland}

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