Preserving Relative Dimension Rankings in the Presence of Noise Using the Box-Counting Algorithm

dc.contributor.advisor Phillips, Joshua
dc.contributor.author Murphy, Michael Colin
dc.contributor.committeemember Seo, Suk
dc.contributor.committeemember Green, Lisa
dc.contributor.department Computer Science en_US
dc.date.accessioned 2016-08-15T15:06:30Z
dc.date.available 2016-08-15T15:06:30Z
dc.date.issued 2016-06-23
dc.description.abstract Fractal dimension is a number that describes the degree of self-similarity, or "complexity", of a particular geometry. In digital image processing, fractal dimension is often used to provide quantitative comparisons between digital images. The Box-Counting Algorithm is one of the more widely used methods for estimating fractal dimension, although it has been shown to be highly sensitive to digital filtering and noise. This research investigates the variability in fractal dimension estimates obtained from the Box-Counting Algorithm as noise is applied to an image. In the case of increasing uniform noise, three distinct relationships emerge between dimensional estimates and their variability. It is then shown how these relationships may be leveraged to improve relative rankings among dimensional estimates when using the Box-Counting Algorithm.
dc.description.degree M.S.
dc.identifier.uri http://jewlscholar.mtsu.edu/handle/mtsu/5022
dc.publisher Middle Tennessee State University
dc.subject Box-Counting
dc.subject Dimension
dc.subject Fractal
dc.subject Image
dc.subject Noise
dc.subject Ranking
dc.subject.umi Computer science
dc.thesis.degreegrantor Middle Tennessee State University
dc.thesis.degreelevel Masters
dc.title Preserving Relative Dimension Rankings in the Presence of Noise Using the Box-Counting Algorithm
dc.type Thesis
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