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Optimization of adaboost algorithm for sonar target detection in a multi-stage ATR system

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dc.contributor.author Lin, Tsung Han (Hank)
dc.date.accessioned 2013-08-07T20:51:14Z
dc.date.available 2013-08-07T20:51:14Z
dc.date.issued 2011-08
dc.identifier.citation NASA Undergraduate Student Research Program (USRP), Pasadena, California, August 2011 en_US
dc.identifier.clearanceno 11-1744
dc.identifier.uri http://hdl.handle.net/2014/43462
dc.description.abstract JPL has developed a multi-stage Automated Target Recognition (ATR) system to locate objects in images. First, input images are preprocessed and sent to a Grayscale Optical Correlator (GOC) filter to identify possible regions-of-interest (ROIs). Second, feature extraction operations are performed using Texton filters and Principal Component Analysis (PCA). Finally, the features are fed to a classifier, to identify ROIs that contain the targets. Previous work used the Feed-forward Back-propagation Neural Network for classification. In this project we investigate a version of Adaboost as a classifier for comparison. The version we used is known as GentleBoost. We used the boosted decision tree as the weak classifier. We have tested our ATR system against real-world sonar images using the Adaboost approach. Results indicate an improvement in performance over a single Neural Network design. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2011. en_US
dc.subject automatic target recognition en_US
dc.subject correlation en_US
dc.subject Adaboost en_US
dc.subject Gentleboost en_US
dc.subject Texton filters en_US
dc.subject false alarm rate en_US
dc.title Optimization of adaboost algorithm for sonar target detection in a multi-stage ATR system en_US
dc.type Other en_US


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