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Neural network target identification system for false alarm reduction

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dc.contributor.author Ye, David
dc.contributor.author Edens, Weston
dc.contributor.author Lu, Thomas T.
dc.contributor.author Chao, Tien-Hsin
dc.date.accessioned 2014-08-07T21:36:53Z
dc.date.available 2014-08-07T21:36:53Z
dc.date.issued 2009-04-13
dc.identifier.citation SPIE Defense, Security and Sensing, Orlando, Florida, April 13-17, 2009 en_US
dc.identifier.clearanceno 09-0736
dc.identifier.uri http://hdl.handle.net/2014/44621
dc.description.abstract A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset. 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, 2009 en_US
dc.subject ATR en_US
dc.subject recognition en_US
dc.title Neural network target identification system for false alarm reduction en_US
dc.type Presentation en_US


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