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| dc.contributor.supervisor | Mpoeleng, Dimane | |
| dc.contributor.supervisor | Hlomani, Hlomani | |
| dc.contributor.author | Tapela, Mbiganyi | |
| dc.date.accessioned | 2021-03-12T12:01:20Z | |
| dc.date.available | 2021-03-12T12:01:20Z | |
| dc.date.issued | 2020-10-22 | |
| dc.identifier.citation | Tapela, M. (2020) FPGA-based image change detection framework , Masters Theses, Botswana International University of Science and Technology: Palapye | en_US |
| dc.identifier.uri | http://repository.biust.ac.bw/handle/123456789/279 | |
| dc.description | Thesis (MSc Computer Science )--Botswana International University of Science and Technology, 2020 | en_US |
| dc.description.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 | en_US |
| dc.description.sponsorship | Botswana International University of Science and Technology (BIUST) | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Botswana International University of Science and Technology (BIUST) | en_US |
| dc.subject | Field Programmable Gate Arrays (FPGA) | en_US |
| dc.subject | Digital images | en_US |
| dc.subject | Image processing | en_US |
| dc.subject | FPGA architecture | en_US |
| dc.subject | MATLA/Simulink | en_US |
| dc.subject | Xilinx system generator | en_US |
| dc.subject | Machine Learning algorithms | en_US |
| dc.subject | Algebra change detection | en_US |
| dc.subject | Algorithms | en_US |
| dc.title | FPGA-based image change detection framework | en_US |
| dc.description.level | msc | en_US |
| dc.description.accessibility | unrestricted | en_US |
| dc.description.department | cis | en_US |
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Faculty of Sciences
This collection is made up of electronic theses and dissertations produced by post graduate students from Faculty of Sciences