Efficient Local Comparison Of Images Using Krawtchouk Descriptors

<p>It is known that image comparison can prove cumbersome in both computational complexity and runtime, due to factors such as the rotation, scaling, and translation of the object in question. Due to the locality of Krawtchouk polynomials, relatively few descriptors are necessary to describe a given image, and this can be achieved with minimal memory usage. Using this method, not only can images be described efficiently as a whole, but specific regions of images can be described as well without cropping. Due to this property, queries can be found within a single large image, or collection of large images, which serve as a database for search. Krawtchouk descriptors can also describe collections of patches of 3D objects, which is explored in this paper, as well as a theoretical methodology of describing nD hyperobjects. Test results for an implementation of 3D Krawtchouk descriptors in GNU Octave, as well as statistics regarding effectiveness and runtime, are included, and the code used for testing will be published open source in the near future. </p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Mathematics and Statistics

Grantor dc:publisher

Eastern Kentucky University

Year

2019

Author dc:creator

• DeVille, Julian

• Artificial Intelligence
• Bioinformatics
• Computer Vision
• Image Processing
• Krawtchouk
• Machine Learning
• Computer Sciences
• Partial Differential Equations

Statement dc:rights

• Copyright 2019 Julian DeVille

Repository record dc:identifier

https://encompass.eku.edu/etd/605

OAI identifier oai:identifier

oai:encompass.eku.edu:etd-1603