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Composite wavelet filters for enhanced automated target recognition

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dc.contributor.author Chiang, Jeffrey N.
dc.contributor.author Zhang, Yuhan
dc.contributor.author Lu, Thomas T.
dc.contributor.author Chao, Tien-Hsin
dc.date.accessioned 2013-02-25T21:43:26Z
dc.date.available 2013-02-25T21:43:26Z
dc.date.issued 2012-04-27
dc.identifier.citation SPIE Symposium on Defense, Security, and Sensing, Baltimore, Maryland, April 23-27, 2012 en_US
dc.identifier.clearanceno 12-2018
dc.identifier.uri http://hdl.handle.net/2014/42777
dc.description.abstract Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper. 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, 2012. en_US
dc.subject automated target recognition en_US
dc.subject wavelet filter en_US
dc.subject sonar video image processing en_US
dc.title Composite wavelet filters for enhanced automated target recognition en_US
dc.type Preprint en_US


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