Abstract

This research has been conducted in order to create a robust, lightweight feature detecting and matching algorithm that builds upon the foundation set by the TreeBASIS algorithm. The goal is to create a color-based version of the TreeBASIS algorithm that uses less hardware resources than the original, is more accurate in its matching capabilities, can successfully be deployed on a resource-limited FPGA platform, and can process in real time. This thesis first presents the newly designed hardware tri-channel FAST Feature Detector that finds features in color. Next the TreeBASIS algorithm is analyzed to discover what improvements can be made in order to reduce its resource usage sufficiently to be able to run on the Xilinx Virtex-4 FX60 while processing color features. At the same time, a software version of the Color TreeBASIS algorithm is compared to the original algorithm and is found to have a 93.3% accuracy on a test set of aerial images, surpassing the accuracy of TreeBASIS by nearly 12%. Then the hardware is meticulously reviewed to discover even more optimizations that allow the Color TreeBASIS algorithm to easily fit onto the Virtex-4 FX60. Next a new application for the matching algorithm, object detection, is introduced as well as the hardware needed to support it. Finally the algorithm is tested on the FPGA system for object detection and is able to successfully identify objects at 60 FPS. Color TreeBASIS proves itself to be more accurate than the TreeBASIS algorithm in the aerial images tests, it ends up using less memory and logic resources than its predecessor, even though it processes three times as much data, it is successfully deployed on a resource-limited FPGA system, and it shows accurate results in real-time object identification, generating an accurate homography 20 to 45% of the time while processing matches at a rate of 60 FPS.

Degree

College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering

Rights

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