Performance Issues in Shape Classification

Autor:	Martha E. Shenton, Samson J. Timoner, W. Eric L. Grimson, William M. Wells, Ron Kikinis, Pollina Golland
Rok vydání:	2002
Předmět:	Support vector machine Statistical shape modeling business.industry Active shape model Radial basis function Pattern recognition Artificial intelligence Quadratic classifier Minimum description length business Classifier (UML) Shape analysis (digital geometry) Mathematics
Zdroj:	Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002 ISBN: 9783540442240 MICCAI (1)
DOI:	10.1007/3-540-45786-0_44
Popis:	Shape comparisons of two groups of objects often have two goals: to create a classifier to separate the groups and to provide information that shows differences between classes. We examine issues that are important for shape analysis in a study comparing schizophrenic patients to normal subjects. For this study, non-linear classifiers provide large accuracy gains over linear ones. Using volume information directly in the classifier provides gains over a classifier that normalizes the data for volume. We compare two different representations of shape: displacement fields and distance maps. We show that the classifier based on displacement fields outperforms the one based on distance maps. We also show that displacement fields provide more information in visualizing shape differences than distance maps.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::37c63c92b85f6a426cf718d8eef17930 https://doi.org/10.1007/3-540-45786-0_44 Zobrazit plný text záznamu