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New methods for computing a shape s orientation and several shape measures for elongation, linearity, circularity, ellipticity, hyperbolicity, and parabolicity of 2D point sets are outlined. These measures are invariant to rotation, scaling, and translation. They are calculated very quickly as well. Among other things, we discover that the definition of elongation highly correlates with the definition of linearity. All of the shape measures are tested on digital curves and compared with existing methods. All of the methods work in real time. The goal was to find a way of identifying basic…mehr

Produktbeschreibung
New methods for computing a shape s orientation and
several shape measures for elongation, linearity,
circularity, ellipticity, hyperbolicity, and
parabolicity of 2D point sets are outlined. These
measures are invariant to rotation, scaling, and
translation. They are calculated very quickly as
well. Among other things, we discover that the
definition of elongation highly correlates with the
definition of linearity. All of the shape measures
are tested on digital curves and compared with
existing methods. All of the methods work in real
time. The goal was to find a way of identifying basic
shapes in images. The motivation was to be able to
use these basic shape descriptors as features in a
computer vision system, where more complicated shapes
can be seen as collections of basic ones. This way, a
computer should one day be able to identify the
objects in even the most complicated scenes we deal
with in our daily lives.
Autorenporträt
The author completed his PhD at the University of Ottawa in the
fall of 2008, in the field of Computer Science. His research
interests lie in Computer Vision, Image Processing, and Pattern
Recognition. His Masters was done at Carleton University, in
2005. Applications of his work are found in surveillance and
security, and medical imaging.