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Neural Network Target Identification System for False Alarm ReductionA 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.
Document ID
Document Type
Conference Paper
External Source(s)
Ye, David
(California Inst. of Tech. Pasadena, CA, United States)
Edens, Weston
(Butler Univ. Indianapolis, IN, United States)
Lu, Thomas T.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Chao, Tien-Hsin
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
May 18, 2015
Publication Date
April 13, 2009
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Meeting Information
SPIE Defense, Security and Sensing(Orlando. FL)
Distribution Limits
No Preview Available