Semantic feature analysis in raster maps

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Title Semantic feature analysis in raster maps
Publication Type thesis
School or College College of Engineering
Department Computing
Author Linton, Trevor Forbes
Date 2009-06-15
Description The extraction of semantic features from images of geographic maps is a difficult and interesting problem. Such features may be robustly segmented through the use of Gestalt principles such as similarity and continuity as realized through the use of tensor voting methods and color histogram analysis, respectively. A framework is developed implementing these Gestalt principles through various algorithms. Linear feature segmentation and intersection detection methods are given, and their performance is demonstrated on a set of real and synthetic map images.
Type Text
Publisher University of Utah
Subject Gestalt principles
Dissertation Institution University of Utah
Dissertation Name MS
Language eng
Relation is Version of Digital reproduction of "Semantic feature analysis in raster maps" J. Willard Marriott Library Special Collections GA2.5 2009 .L56
Rights Management © Trevor Forbes Linton
Format application/pdf
Format Medium application/pdf
Format Extent 83,411 bytes
Identifier us-etd2,119194
Source Original: University of Utah J. Willard Marriott Library Special Collections
Conversion Specifications Original scanned on Epson GT-30000 as 400 dpi to pdf using ABBYY FineReader 9.0 Professional Edition
ARK ark:/87278/s6mc9dns
Setname ir_etd
ID 193897
Reference URL https://collections.lib.utah.edu/ark:/87278/s6mc9dns
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