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iECO learned matched filters for automatic target recognition in synthetic midwave infrared imagery
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Paper Abstract

Object recognition is a critical component in most computer vision applications, specifically image classification tasks. Often, it is desired to design an approach that either learns from the data directly or extracts discriminative features from the imagery that can be used for object classification. Most active research in the field of computer vision is concerned with machine learning at some level, whether it be a completely automated process from start to finish via deep learning strategies, or the extraction of human-derived features from the imagery that is subjected to a machine learning-based classifier. However, there are numerous applications in which a particular known object is of interest. In such a setting where a relatively specific object and scene are known a priori, one can develop an extremely robust automatic target recognition (ATR) system using matched filtering. Herein, we consider the use of machine learning to help identify a near-optimal template for matched filtering for a given problem. Specifically, the improved Evolution Constructed (iECO) framework is employed to learn the discriminative target signature(s) to define the template that leads to improved ATR performance in terms of accuracy and a reduced false alarm rate (FAR). Experiments are conducted on ideal synthetic midwave infrared imagery, and results are reported via receiver operating characteristic curves.

Paper Details

Date Published: 14 May 2019
PDF: 11 pages
Proc. SPIE 10988, Automatic Target Recognition XXIX, 1098816 (14 May 2019); doi: 10.1117/12.2518741
Show Author Affiliations
Stanton R. Price, U.S. Army Engineer Research and Development Ctr. (United States)
Steven R. Price, Mississippi College (United States)


Published in SPIE Proceedings Vol. 10988:
Automatic Target Recognition XXIX
Riad I. Hammoud; Timothy L. Overman, Editor(s)

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