Ground-target classification using robust active contour segmentation

Jean-Francois Bonnet; Daniel Duclos; Georges Stamon; Roger A. Samy

doi:10.1117/12.359996

24 August 1999 Ground-target classification using robust active contour segmentation

Jean-Francois Bonnet, Daniel Duclos, Georges Stamon, Roger A. Samy

Proceedings Volume 3718, Automatic Target Recognition IX; (1999) https://doi.org/10.1117/12.359996
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

This paper deals with a Ph-D work about Automatic Target Recognition in Infrared aerial image sequences. The targets to be recognized are ground military vehicles like tanks or lorries. . . During the first step of the Automatic Target Recognition system simulation, the targets are segmented and tracked using an innovative active contour model. The active contour is based on snakes, robust statistics and it uses temporal information on the deformation of the target, such information being acquired during the sequence. This is performed in order to improve the tracking and the recognition to follow. The second step of the ATR system is the on-line recognition of the tracked and segmented objects. To that end, we use two modules based on pre-trained artificial neural networks. One is dedicated to target classification, the other to target identification. Both receive as input the Fourier descriptor of the extracted target shape. This method is validated both on Air-To-Ground IR seeker images and Ground IR camera images.

Citation Download Citation

Jean-Francois Bonnet, Daniel Duclos, Georges Stamon, and Roger A. Samy "Ground-target classification using robust active contour segmentation", Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); https://doi.org/10.1117/12.359996

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