Abstract:
This research presents a new approach in modeling and implementing a framework for linear
image change detection using Field Programmable Gate Arrays (FPGA) based implementations.
Detecting changes between digital images of the same location or region of interest have attracted
huge interest in research due to its wide range of applications such as medical diagnosis,
astronomy and forensics. In recent years, the steady increase in the use of hardware
implementation in image processing, especially the FPGA architecture that has proven to be
superior in performance and cost-efficiency when compared to the conventional computer
systems. The problem addressed by this research is the lack of an FPGA based generic and
standardized change detection framework that executes image change detection at a low
computational cost, using low processing resources like memory and power consumption.
The framework proposed in this research work is focused on the image differencing, which is an
algebra change detection technique. Image edge detection, image enhancement and restoration
methods were employed in the framework implementations using MATLA/Simulink and the Xilinx
system generator. The success of the framework implementation proved that the objectives of
the thesis were achieved and has been verified by the experimental results. While the
implementation was successful in producing expected results, there is a need for more
experiments to explore different filtering algorithms that could improve the results. Statistical
forms of data interpretation such as Machine Learning algorithms could improve complex image
difference results analysis
