Date Approved
7-26-2021
Graduate Degree Type
Thesis
Degree Name
Engineering (M.S.E.)
Degree Program
School of Engineering
First Advisor
Dr. Chirag Parikh
Second Advisor
Dr. Christian Trefftz
Third Advisor
Dr. Nabeeh Kandalaft
Academic Year
2020/2021
Abstract
Image stitching is a process where two or more images with an overlapping field of view are combined. This process is commonly used to increase the field of view or image quality of a system. While this process is not particularly difficult for modern personal computers, hardware acceleration is often required to achieve real-time performance in low-power image stitching solutions. In this thesis, two separate hardware accelerated image stitching solutions are developed and compared. One solution is accelerated using a Xilinx Zynq UltraScale+ ZU3EG FPGA and the other solution is accelerated using an Nvidia RTX 2070 Super GPU. The image stitching solutions implemented in this paper increase the system’s field of view and involve the end-to-end process of feature detection, image registration, and image mixing. The latency, resource utilization, and power consumption for the accelerated portions of each system are compared and each systems tradeoffs and use cases are considered.
ScholarWorks Citation
Edgcombe, Joshua David, "Hardware Acceleration in Image Stitching: GPU vs FPGA" (2021). Masters Theses. 1018.
https://scholarworks.gvsu.edu/theses/1018
