This research thesis starts off with a basic introduction to optimization and image processing. Because there are several different tools to apply optimization in image processing applications, we started researching one category of mathematical optimization techniques, namely Convex Optimization. This thesis provides a basic background consisting of mathematical concepts, as well as some challenges of employing Convex Optimization in solving problems. One major issue is to be able to identify the convexity of the problem in a potential application (Boyd). After spending a couple of months researching and learning Convex Optimization, my advisor and I decided to go on a different route. We decided to use Heuristic Optimization techniques instead, and in particular, Genetic Algorithms (GA). We also conjectured that the application of GA in image processing for the purpose of object matching could potentially yield good results. As a first step, we used MATLAB as the programming language, and we wrote the GA code from scratch. Next, we applied the GA algorithm in object matching. More specifically, we constructed specific images to demonstrate the effectiveness of the algorithm in identifying objects of interest. The results presented in this thesis indicate that the technique is capable of identifying objects under noise conditions.
| Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:honors_theses-1130 |
| Date | 01 April 2019 |
| Creators | Chapagain, Prerak |
| Publisher | ScholarWorks@UNO |
| Source Sets | University of New Orleans |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Senior Honors Theses |
| Rights | http://creativecommons.org/licenses/by-sa/4.0/ |
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