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Deep Convolutional Neural Networks With Integrated Quadratic Correlation Filters for Automatic Target Recognition
AbstractAutomatic target recognition involves detecting and recognizing potential targets automatically, which is widely used in civilian and military applications today. Quadratic correlation filters were introduced as two-class recognition classifiers for quickly detecting targets in cluttered scene environments. In this paper, we introduce two methods that integrate the discrimination capability of quadratic correlation filters with the multi-class recognition ability of multilayer neural networks. For mid-wave infrared imagery, the proposed methods are demonstrated to be multi-class target recognition classifiers with very high accuracy.
Related Material
[pdf][bibtex]@InProceedings{Millikan_2018_CVPR_Workshops,
author = {Millikan, Brian and Foroosh, Hassan and Sun, Qiyu},
title = {Deep Convolutional Neural Networks With Integrated Quadratic Correlation Filters for Automatic Target Recognition},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}
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