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Raster to vector conversion in a local, exact and near optimal manner

A dissertation submitted to the Faculty of Science, University
of the Witwatersrand, Johannesburg, in partial fulfillment of the
requirements for the degree of Master of Science. Pretoria 1991. / Remote sensing can be used to produce maps of land-cover, but to
be of use to the GIS community these maps must first be
vectorized in an intelligent manner.
Existing algorithms suffer from the defects of being slow, memory
intensive and producing vast quantities of very short vectors.
Furthermore if these vectors are thinned via standard algorithms,
errors are introduced.
The process of vectorizing raster maps is subject to major
ambiguities. Thus an infinite family of vector maps ccrresponds
to each raster map. This dissertation presents an algorithm for
converting raster maps in a rapid manner to accurate vector maps
with a minimum of vectors.
The algorithm converts raster maps to vector maps using local
information only, (a two by two neighbourhood). the method is
"exact" in the sense that rasterizing the resulting polygons
would produce exactly the same raster map, pixel for pixel.
The method is "near optimal" in that it produces, in a local
sense, that "exacb" vector map having the least number of
vectors.
The program is built around a home-grown object oriented
Programming System (OOPS) for the C programming language. The
main features of the OOPS system, (called OopCdaisy), are virtual
and static methods, polymorphism, generalized containers,
container indices and thorough error checking, The following
general purpose objects are implemented with a large number of
sophistiated methods :- Stacks, LIFO lists, scannable containers
with indices, trees and 2D objects like points, lines etc. / AC2017

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/22098
Date January 1991
CreatorsCarter, John Andrew
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeThesis
FormatOnline resource (68 leaves), application/pdf

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