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 2017.
• 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: 25 October 2017.
• 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 session during week 10.
• 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 EECS 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 27 November 2017; in lab demonstrations will be conducted during the final lab session as well as the last several lecture periods.

Electrical Engineering and Computer Science Department laboratory facilites will be available for support of projects. Documentation of the lab is available under the Syllabus link on the sidebar to this page.

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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.

• Adaptive stereo vision: In our study of stereopsis, we will learn that a useful strategy is to begin our estimation procedures with coarse image data (e.g., imagery with low spatial resolution) and subsequently refine our solution through systematic incorporation of more refined image data (e.g., imagery with higher spatial resolution). We will refer to this paradigm as course-to-fine refinement. An interesting question that arises in this paradigm is how to decide on the level of refinement that is appropriate for a given image or even a given image region. For this project, the student will explore methods for automatically adapting the coarse-to-fine refinement of stereo estimates based on the input binocular image data and implement as well as test at least one such procedure.

• Primitive event recognition: Image sequences comprise a vast amount of data. For timely processing, it is critical that early operations begin to recognize significant patterns in this data. For example, when processing a video sequence, it might be desirable to make distinctions regarding those portions of the imagery that correspond to moving objects so that subsequent processing might attend to such regions. For this project, the student will suggest and investigate a set of simple, dynamic patterns or events that it might be advantageous to support through early processing (e.g., what is moving, what is not, what is noise, etc.). The student will develop an analysis that shows how the patterns can be distinguished on the basis of simple vision operations as well as implement and test corresponding algorithms.

• Module combination: Typically, a solitary computer vision module provides incomplete information about the world; however, taken in tandem (two or more) modules might combine forces to provide a better understanding of the impinging environment. For example, binocular stereo typically performs best in regions with well define features or texture patterns, but poorly in smoothly shaded regions; in contrast shape from shading can perform reasonably in smoothly shaded regions, but less well in highly textured regions. For this project, students will select a pair of complimentary vision modules (e.g., stereo and shading, feature-based and area-based image correspondence/matching for stereo, binocular stereo and motion, etc.) and study how they can be combined in an advantageous fashion. The student will develop an analysis that shows how the modules can be combined as well as implement and test corresponding algorithms.

• Algorithm comparisons: For any given research or application topic in computer vision there is more than one possible approach. For example, many different approaches to optical flow estimation have been developed. For this project, students will consider a particular topic (e.g., binocular stereo correspondence, optical flow estimation, shape from shading, etc.) and select at least two algorithms that have been developed for this topic. The student will compare the selected algorithms both analytically (to develop a theoretical understanding of their relationships) and empirically (to develop a practical understanding of their relationships. Since for this project the student will be comparing extant algorithms, it might be possible to acquire the necessary software without actually implementing the algorithms per se (e.g., from the web); however, probably more will be learned if the student implements their own versions. A restriction on this topic is that comparison of algorithms for edge detection is not an allowable topic; there has been a great deal of research on this topic, which will make it too difficult for students to make a novel contribution.