NDLI: Parallel algorithms for automatic target recognition using Co/sub 2/ laser radar images

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Proceedings of the Seventh Workshop on Multidimensional Signal Processing

Sixth Multidimensional Signal Processing Workshop,

Sixth Multidimensional Signal Processing Workshop (Cat. No.89TH0290-7)

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Parallel algorithms for automatic target recognition using Co/sub 2/ laser radar images

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Parallel algorithms for automatic target recognition using Co/sub 2/ laser radar images

Content Provider	IEEE Xplore Digital Library
Author	Sullivan, D.J. Forman Jr., A.V.
Copyright Year	1989
Description	Author affiliation: Martin Marietta Electron. Syst., Orlando, FL, USA (Sullivan, D.J.; Forman, A.V., Jr.)
Abstract	A parallel implementation of algorithms to achieve viewpoint invariant target recognition from laser radar images is described. The active/passive CO/sub 2/ laser radar sensor used provides pixel-registered thermal, visual, relative range and artifacts and detects edges. Viewpoint invariance obtained using the translation invariant property of the Fourier transform transforms 2-D rotations and scale changes into translations. This scene representation provides 2-D viewpoint invariance and is used as input to a neural network that has been trained to recognize representative views of the targets of interest. The system is implemented on the Geometric Arithmetic Parallel Processor (GAPP), a massively parallel single-instruction-multiple-data (SIMD) computer that contains a 216*384 array of processing elements. Results obtained by processing real lasers radar images are presented.
File Size	42003
File Format	PDF
DOI	10.1109/MDSP.1989.97026
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1989-09-06
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parallel algorithms Target recognition Laser radar Radar imaging Gas detectors Image edge detection Fourier transforms Passive radar Thermal sensors Radar detection
Content Type	Text
Resource Type	Article

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