Author Type

Graduate Student

Date of Award

2018

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Su, Hongbo

Abstract

In this research, image segmentation and visual odometry estimations in real time are addressed, and two main contributions were made to this field. First, a new image segmentation and classification algorithm named DilatedU-NET is introduced. This deep learning based algorithm is able to process seven frames per-second and achieves over 84% accuracy using the Cityscapes dataset. Secondly, a new method to estimate visual odometry is introduced. Using the KITTI benchmark dataset as a baseline, the visual odometry error was more significant than could be accurately measured. However, the robust framerate speed made up for this, able to process 15 frames per second.

Extent

57 p.

Comments

Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2018.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00005990

Format

pdf

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