Matthew Stites

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Pattern recognition of amorphously shaped objects such as gas plumes, oil spills, or epidemiological spread is difficult because there is no definite shape to match. We consider detection of such amorphously shaped objects using a neighborhood model which operates on a concept of loose spatial contiguity: there is a significant probability that a pixel(More)
This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or(More)
Incorporating Spatial Information into Gas Plume Detection in Hyperspectral Imagery by Cameron S. Grant, Master of Science Utah State University, 2010 Major Professor: Dr. Jacob H. Gunther Department: Electrical and Computer Engineering Detection of chemical plumes in hyperpsectral data is a problem having solutions that focus on spectral information. These(More)
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