Constraints in distortion-invariant target recognition system simulation

Khan M. Iftekharuddin; Md Abdur Razzaque

doi:10.1117/12.408559

29 November 2000 Constraints in distortion-invariant target recognition system simulation

Khan M. Iftekharuddin, Md Abdur Razzaque

Proceedings Volume 4114, Photonic Devices and Algorithms for Computing II; (2000) https://doi.org/10.1117/12.408559
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

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.

Citation Download Citation

Khan M. Iftekharuddin and Md Abdur Razzaque "Constraints in distortion-invariant target recognition system simulation", Proc. SPIE 4114, Photonic Devices and Algorithms for Computing II, (29 November 2000); https://doi.org/10.1117/12.408559

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