Date of Award
2011
Publication Type
Master Thesis
Degree Name
M.A.Sc.
Department
Electrical and Computer Engineering
Keywords
Engineering, Electronics and Electrical.
Supervisor
Q. M. Jonathan, Wu (Electrical and Computer Engineering)
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
Real-time embedded vision systems can be used in a wide range of applications and therefore the demand has been increasing for them. In this thesis, an FPGA-based embedded vision system capable of recognizing objects in real time is presented. The proposed system architecture consists of multiple Intellectual Properties (IPs), which are used as a set of complex instructions by an integrated 32-bit CPU Microblaze. Each IP is tailored specifically to meet the needs of the application and at the same time to consume the minimum FPGA logic resources. Integrating both hardware and software on a single FPGA chip, this system can achieve the real-time performance of full VGA video processing at 32 frames per second (fps). In addition, this work comes up with a new method called Dual Connected Component Labelling (DCCL) suitable for FPGA implementation.
Recommended Citation
Xiong, Jian, "FPGA Implementation of Blob Recognition" (2011). Electronic Theses and Dissertations. 148.
https://scholar.uwindsor.ca/etd/148