Two methods of designing a point classifier are discussed in this paper, one is a binary decision tree classifier based on the Fisher's linear discriminant function as a decision rule at each nonterminal node, and the other is a contextual classifier which gives each pixel the highest probability label given some substantially sized context including the pixel.
Experiments were performed both on a simulated image and real images to illustrate the improvement of the classification accuracy over the conventional single-stage Bayes classifier under Gaussian distribution assumption. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/53076 |
| Date | January 1985 |
| Creators | Joo, Hyonam |
| Contributors | Electrical Engineering |
| Publisher | Virginia Polytechnic Institute and State University |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Type | Thesis, Text |
| Format | vi, 93 leaves, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | OCLC# 12655126 |
Page generated in 0.0022 seconds