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1 April 1992 Highly automated nonparametric statistical learning for autonomous target recognition
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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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Keith C. Drake "Highly automated nonparametric statistical learning for autonomous target recognition", Proc. SPIE 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges, (1 April 1992);


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