Pattern Recognition and Image Analysis Extensions to the IE2000 IPToolKit

This research project produced results of both fundamental and practical benefit, including a quantitative description of the finite sample accuracy of the k nearest neighbor classifier for a large family of smooth pattern recognition problems, a new theoretical justification for use of a weighted e...

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Bibliographic Details
Main Author: Snapp, Robert R
Format: Report
Language:English
Subjects:
ALGORITHMS
BAYES THEOREM
BAYESIAN CLASSIFIERS
Cybernetics
IMAGE PROCESSING
K NEAREST NEIGHBOR CLASSIFIERS
MULTISPECTRAL
PATTERN RECOGNITION
PE62702F
SOFTWARE TOOLS
STATISTICAL SAMPLES
Statistics and Probability
WUAFRL48130001
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Description
Summary:This research project produced results of both fundamental and practical benefit, including a quantitative description of the finite sample accuracy of the k nearest neighbor classifier for a large family of smooth pattern recognition problems, a new theoretical justification for use of a weighted euclidean metric as a similarity function, the development of a procedure for estimating the Bayes risk of practical problems, and the development of a fast approximation of a kappa nearest neighbor classifier, called the labeled cell classifier. The research resulted in a stand-alone X Windows software application, called pstool, that allows users to interactively construct training sets from multispectral digital images, six refereed conference publications, a 40 page technical report, and a journal publication in the Annals of Statistics. Seven graduate students at the University of Vermont participated in this project: four receiving Masters degrees, and one, a Ph.D in Electrical Engineering.