Utilizing Spatial Locality of ColourFAST Features for GPU-Accelerated Object Recognition
Date
Authors
Da Fonseca, Eleanor
Supervisor
Ensor, Andrew
Hall, Seth
Item type
Thesis
Degree name
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