Students will be required to complete a computer vision project as a part of this course. This project will allow students to gain “hands on” experience with a computer vision research topic. Typically, projects will involve analytic, programming and testing components. Projects will consist of four parts, as follows.

• White Paper: Each student will select a particular topic for study and inform the instructor of their choice via a short (1-2 sentence) written description. The final topic selection is subject to the instructor's approval. A list of suggested topics is provided below. Topics other than those listed may be acceptable; however, it a good idea to check with the instructor as early as possible for such cases. Due date: 4 October 2013.
• Proposal: Each student will submit to the instructor a brief (1 page) proposal for their project. This will extend on the topic selection by (i) motivating the area of investigation (explaining why it is interesting), (ii) describing an approach (providing a plan of attack), including what problem analyses, implementations and testing you intend to perform and (iii) suggesting possible outcomes. Due date: 21 October 2013.
• Site Visit: Each student will make a brief verbal report of their progress; students are encouraged to provide preliminary demonstrations of any software that is being developed at this time. Due Date: Presentations will be made in the lab (1006B LAS), 18 November 2013.
• Final Demo: A final written report (approximately 5 pages) that documents the results of the student's investigations will be will be delivered to the instructor. Further, each student will provide a demonstration/discussion of their project results in front of the class. The software program that results as part of the project must be callable from the command line with argument of pgm image(s), as appropriate to the particular problem considered: This will allow the instructor to test the program during the final demo. For students enrolled in CSE 5323 only, the final written report also will be required to have an appendix (beyond the 5 page main body of the report), which is comprised of an annotated bibliography of references to the primary literature that is related to the project. Due Date: Written reports due at start of class 29 November 2013; in lab demonstrations will be conducted during the last several lecture periods beginning the same day.

Computer Science and Engineering Department laboratory facilites will be available for support of projects. Details TBA.

Following is a list of suggested topics for this course as well as brief descriptions. If further discussion of any of these topics is desired, then see the instructor.

• Image formation: Since in computer vision we seek to recover information about the world from images, it is important to understand how the world information was projected into the images. Correspondingly, in lecture and in the textbook, various models of image formation have been described. For this topic, the student will further explore image formation. As a specific direction for investigation, one might study the effects of the approximations that are involved for various models of image projection (e.g., a quantitative comparison of the geometric ramifications of perspective vs. weak perspective vs. orthographic projection). Alternatively, one might embark on a quantitative exploration of optical aberrations (geometric and/or radiometric). The student also is welcome explore models of image formation that have not been covered in class (e.g., image formation using transmittance image formation, etc.). In any case, an appropriate methodology for this project would be to couple formal mathematical analysis with computer simulations of the models of concern.

• Image preprocessing: The output provided by a computer vision algorithm can be greatly improved via appropriate preprocessing of the input imagery. For example, techniques can be applied to suppress noise; alternatively, features of particular interest can be enhanced. For this topic, the student will select a computer vision algorithm that takes as its input one or more images (e.g., edge detection, optical flow estimation, etc.) and quantitatively evaluate the effects of one or more image preprocessing steps. Since the emphasis here is on how the preprocessing can help improve a computer vision algorithm of interest, it might be possible to acquire the necessary software for the algorithm whose performance you wish to improve without actually implementing it (e.g., from the web). In contrast, the preprocessing operations should be implemented by the student.