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Neural network post-processing of grayscale optical correlatorIn this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.
Document ID
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Lu, Thomas T
Hughlett, Casey L.
Zhoua, Hanying
Chao, Tien-Hsin
Hanan, Jay C.
Date Acquired
August 23, 2013
Publication Date
August 4, 2005
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Meeting Information
Meeting: International Society for Optical Engineering (SPIE) Optics & Photonics Conference
Location: San Diego, CA
Country: United States
Start Date: August 4, 2005
Distribution Limits
grayscale optical correlator
pattern recognition
neural network

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