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 |