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Proceedings Paper

Highly automated nonparametric statistical learning for autonomous target recognition
Author(s): Keith C. Drake
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Paper Abstract

Image pattern recognition is presented as three sequential tasks: feature extraction, object plausibility estimation (determining class likelihoods), and decision processing. Several data- driven techniques yield discriminant functions to produce object plausibility estimates from image features, including traditional statistical methods and neural network approaches. A statistical learning algorithm which integrates multiple-regression algorithms, functional networking strategies, and a statistical modeling criterion is presented. It provides a non- parametric learning algorithm for the synthesis of discriminant functions. Image understanding tasks such as object plausibility estimation require robust modeling techniques to deal with the uncertainty prevalent in real-world data. Specifically, these complex tasks require robust and cost-effective techniques to successfully integrate multi-source information. AbTech and others have shown that implementation of the statistical learning concepts discussed provide a modeling approach ideal for information fusion tasks such as autonomous object recognition for tactical targets and space-based assets. The results of using this approach to develop a prototype aircraft recognition system is presented.

Paper Details

Date Published: 1 April 1992
PDF: 10 pages
Proc. SPIE 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges, (1 April 1992); doi: 10.1117/12.58068
Show Author Affiliations
Keith C. Drake, AbTech Corp. (United States)

Published in SPIE Proceedings Vol. 1623:
The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges
Joan B. Lurie, Editor(s)

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