Publication Date
2016
Document Type
Thesis
Committee Members
Arthur Goshtasby (Advisor), Juan Vasquez (Committee Member), Thomas Wischgoll (Committee Member)
Degree Name
Master of Science in Mechanical Engineering (MSME)
Abstract
Many video processing applications, such as motion detection and tracking, rely on accurate and robust alignment between consecutive video frames. Traditional approaches to video image registration, such as pyramidal Kanade-Lucas-Tomasi (KLT) feature detection and tracking are fast and subpixel accurate, but are not robust to large inter-frame displacements due to rotation, scale, or translation. This thesis presents an alternative hybrid approach using normalized gradient correlation (NGC) in the frequency domain and normalized cross-correlation (NCC) in the spatial domain that is fast, accurate, and robust to large displacements. A scale space search is incorporated into NGC to enable more consistent recovery of scale factors up to 6. Results show that the scale space enhanced NGC improves performance in both speed and maximum scale recovery. The proposed hybrid approach is compared to KLT and results demonstrate a significant improvement in robustness in exchange for a slight reduction in accuracy.
Page Count
140
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2016
Copyright
Copyright 2016, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
