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

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dc.contributor.advisor Matsakis, Pascal Kemp, Jason 2019-06-06T16:09:54Z 2019-06-06T16:09:54Z 2019-05 2019-06-03 2019-06-06
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher University of Guelph en_US
dc.rights Attribution-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject Relative Position Descriptor en_US
dc.subject Digital Image Processing en_US
dc.subject Spherical Point Distribution en_US
dc.subject Phi Descriptor en_US
dc.subject Force Histogram en_US
dc.subject Thomson Problem en_US
dc.subject Direction Set en_US
dc.title Contributions to Relative Position Descriptor Computation in the case of Vector Objects en_US
dc.type Thesis en_US Computer Science en_US Master of Science en_US School of Computer Science en_US University of Guelph en_US

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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
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