Shape Descriptors
Aktas, Mehmet Ali
Date: 18 October 2012
Thesis or dissertation
Publisher
University of Exeter
Degree Title
PhD in Computer Science
Abstract
Every day we recognize a numerous objects and human brain can recognize
objects under many conditions. The way in which humans are able
to identify an object is remarkably fast even in different size, colours or
other factors. Computers or robots need computational tools to identify
objects. Shape descriptors are one of the tools ...
Every day we recognize a numerous objects and human brain can recognize
objects under many conditions. The way in which humans are able
to identify an object is remarkably fast even in different size, colours or
other factors. Computers or robots need computational tools to identify
objects. Shape descriptors are one of the tools commonly used in image
processing applications. Shape descriptors are regarded as mathematical
functions employed for investigating image shape information. Various
shape descriptors have been studied in the literature. The aim of this
thesis is to develop new shape descriptors which provides a reasonable
alternative to the existing methods or modified to improve them.
Generally speaking shape descriptors can be categorized into various
taxonomies based on the information they use to compute their measures.
However, some descriptors may use a combination of boundary
and interior points to compute their measures. A new shape descriptor,
which uses both region and contour information, called centeredness
measure has been defined. A new alternative ellipticity measure and
sensitive family ellipticity measures are introduced. Lastly familiy of
ellipticity measures, which can distinguish between ellipses whose ratio
between the length of the major and minor axis differs, have been presented.
These measures can be combined and applied in different image
processing applications such as image retrieval and classification. This
simple basis is demonstrated through several examples.
Doctoral Theses
Doctoral College
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