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Proceedings Paper

Constraints in distortion-invariant target recognition system simulation
Author(s): Khan M. Iftekharuddin; Md Abdur Razzaque
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Paper Abstract

Automatic target recognition (ATR) is a mature but active research area. In an earlier paper, we proposed a novel ATR approach for recognition of targets varying in fine details, rotation, and translation using a Learning Vector Quantization (LVQ) Neural Network (NN). The proposed approach performed segmentation of multiple objects and the identification of the objects using LVQNN. In this current paper, we extend the previous approach for recognition of targets varying in rotation, translation, scale, and combination of all three distortions. We obtain the analytical results of the system level design to show that the approach performs well with some constraints. The first constraint determines the size of the input images and input filters. The second constraint shows the limits on amount of rotation, translation, and scale of input objects. We present the simulation verification of the constraints using DARPA's Moving and Stationary Target Recognition (MSTAR) images with different depression and pose angles. The simulation results using MSTAR images verify the analytical constraints of the system level design.

Paper Details

Date Published: 29 November 2000
PDF: 12 pages
Proc. SPIE 4114, Photonic Devices and Algorithms for Computing II, (29 November 2000); doi: 10.1117/12.408559
Show Author Affiliations
Khan M. Iftekharuddin, North Dakota State Univ. (United States)
Md Abdur Razzaque, North Dakota State Univ. (United States)


Published in SPIE Proceedings Vol. 4114:
Photonic Devices and Algorithms for Computing II
Khan M. Iftekharuddin; Abdul Ahad Sami Awwal, Editor(s)

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