Using sparse parametrization of deformation fields as means to classification

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Title Using sparse parametrization of deformation fields as means to classification
Publication Type thesis
School or College College of Engineering
Department Computing
Author Tirpankar, Nishith
Date 2013-05
Description Large Deformation Di eomorphic Metric Mapping is a powerful technique which has been used to quantify variations in anatomical structures in medical images. This allows us to compare various images within and across a populations of classes using the underlying deformation eld which maps each image with the representative images of the class. The deformation eld can be described by a low-dimensional control point parameterization. We investigate the potential of this low-dimensional parameterization in classi cation and study the e ect of the underlying classi er parameters on the classi cation accuracy.
Type Text
Publisher University of Utah
Subject Atlas Estimation; Classification; Diffeomorphic Deformation; LDDMM; Optimization; Registration
Dissertation Institution University of Utah
Dissertation Name Master of Science
Language eng
Rights Management Copyright © Nishith Tirpankar 2013
Format application/pdf
Format Medium application/pdf
Format Extent 1,766,062 bytes
ARK ark:/87278/s6sf3b1v
Setname ir_etd
ID 195922
Reference URL https://collections.lib.utah.edu/ark:/87278/s6sf3b1v
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