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Contributions to Relative Position Descriptor Computation in the case of Vector Objects

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Title: Contributions to Relative Position Descriptor Computation in the case of Vector Objects
Author: Kemp, Jason
Department: School of Computer Science
Program: Computer Science
Advisor: Matsakis, Pascal
Abstract: Two new algorithms are introduced, both related to Relative Position Descriptors (RPDs) in the case of vector objects. The first, The Great Circle distribution, presents a new spherical point distribution. This algorithm distributes points over the surface of a sphere, ensuring points lie on the minimum number of great circles possible, while keeping the points distributed evenly. Multiple evenness measures are used to compare evenness with multiple common spherical point distribution techniques. This distribution is designed as a direction set for 3D RPDs, where each point represents a direction, and will improve the efficiency of 3D vector RPDs. The second new algorithm builds on the φ-descriptor, a recent RPD. In this paper the first algorithm to calculate the φ-descriptor for 2D vector objects is proposed and tested. The results are compared against the existing 2D raster φ-descriptor. The new algorithm is intended to show the versatility of the φ-descriptor.
URI: http://hdl.handle.net/10214/16240
Date: 2019-05
Rights: Attribution-NoDerivatives 4.0 International
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Attribution-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NoDerivatives 4.0 International