Project proposals
The following are several of the project proposals that you will work until the end of the semester. You must select one of the following topics by May 4th, or you may choose a different one that is related to your master’s thesis. Please email me with the topic you have chosen and the team members. If you would like to do something different, please email me, and we can talk about it.
- Optical Character Recognition (OCR)
- Try developing your own basic OCR app. For example just with printed numbers. You could grab (an solve) a printed sudoku.
- Augmented Reality (AR)
- Project 3D objects (from CAD?) onto flat surface. Best if done on video with moving camera. You can use a sheet of paper or a TAG on top of flat surface for reference. The basis of this problem is to estimate pose of camera using information from the reference plane. You can use the five point target developed in the course.
- Image content classification
- Classify the contents of an image based on color, spatial and/or texture appearance. For example, from aerial images your application could classify regions as roads, trees, etc.
General Guidelines
The general requirement is that you implement the code at least with a simulated object, but best if you acquire images in a controlled environment. You may reuse code from other sources but cite them properly. Your final report should be between 6 - 8 pages using the IEEE template.
Report
The following is a suggested structure for the report:
- Title, Author(s)
- Abstract: It should not be more than 300 words
- Introduction: this section introduces your problem and the overall plan for approaching your problem
- Background/Related Work: This section discusses relevant literature for your project
- Approach: This section details the framework of your project. Be specific, which means you might want to include equations, figures, plots, etc
- Experiment: This section begins with what kind of experiments you’re doing, what kind of dataset(s) you’re using, and what is the way you measure or evaluate your results. It then shows in details the results of your experiments. By details, we mean both quantitative evaluations (show numbers, figures, tables, etc.) as well as qualitative results (show images, example results, etc.).
- Conclusion: What have you learned? Suggest future ideas.
- References: This is necessary.