Neural network target identification system for false alarm reduction
- Others:
- Casasent, David P.
- Chao, Tien-Hsin
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.
Additional Information
© 2009 Society of Photo-Optical Instrumentation Engineers (SPIE). This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration (NASA), and was sponsored under the SURF and USRP programs through NASA. The authors would like to acknowledge Mr. Tristan Winterhalter, Mr. Stephen Williams, and Mr. Oliver Johnson for useful help and discussions.Attached Files
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Additional details
- Eprint ID
- 95524
- Resolver ID
- CaltechAUTHORS:20190515-153249012
- NASA/JPL/Caltech
- Created
-
2019-05-16Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Series Name
- Proceedings of SPIE
- Series Volume or Issue Number
- 7340