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Set Inversion Fractals

Authors: Boreland, Bryson;

Set Inversion Fractals

Abstract

In 2000, Frame and Cogevina introduced a method for constructing fractals using circle inversion maps. The literature focuses on the graphical aspect of such fractals, without presenting a careful development of the underlying mathematical framework. In this thesis, we present such a framework, making a strong connection to iterated function systems (IFS) theory. Our final result establishes that the set valued system of circle inversion maps induced by a collection of possibly overlapping circles in the plane has a unique set attractor. We then establish a similar mathematical framework in the setting of non-touching star-shaped sets. We present graphical examples for both settings using the chaos game. Finally, fractals literature develops the well-known concept of local iterated function systems with grey-level maps, with applications to image processing. We follow this path to establish a framework that uses local circle inversion maps as the functions. We demonstrate the results with examples. Ontario Graduate Scholarship

Country
Canada
Related Organizations
Keywords

Signal Approximation, Fractals, Grey-Level Maps, Star-Shaped Sets, Banach's Fixed Point Theorem, Circle Inversion, Iterated Function Systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green