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Utilizing Spatial Locality of ColourFAST Features for GPU-Accelerated Object Recognition

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Whole thesis(51.59 MB)

Date

2015

Authors

Supervisor

Ensor, Andrew
Hall, Seth

Item type

Thesis

Degree name

Master of Science

Journal Title

Journal ISSN

Volume Title

Publisher

Auckland University of Technology

Abstract

ColourFAST is an alternative technique to FAST developed by Ensor and Hall used to extract feature point descriptors from an image based on colour change values. The extracted descriptor is compact and, therefore, efficient to compute and match. The purpose of this thesis is to extend the Colour-FAST feature descriptor from a 4-dimensional vector to a 6-dimensional vector to improve feature point matching accuracy. This is achieved by incorporating spatial locality to gain a sense of the shape of an object alongside its colour change information. The main focus is designing, developing and testing feature point matching algorithms specifically architected for the GPU pipeline with an emphasis on accuracy while maintaining high throughput.

Description

Keywords

GPU, GPU-acceleration, ColourFAST, Object recognition, Spatial locality, Eleanor Da Fonseca, Seth Hall, Andrew Ensor, Feature matching, Computer vision, Parallel computing, GPGPU

Source

DOI

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Permanent link

https://hdl.handle.net/10292/8933

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Masters Theses