COS 429 - Computer Vision

Fall 2016

Final Project

The final assignment for this semester is to do an in-depth project implementing a nontrivial vision system. You will be expected to design a complete pipeline, read up on the relevant literature, implement the system, and evaluate it on real-world data. You will work in small groups (2-4 people), and must deliver

• A short (2 paragraph) proposal and your team members due December 18. Please submit one proposal per team in plain-text, HTML, or PDF format to the Dropbox link here.
• A report on your system due Jan. 17. This should include sections on previous work, design and implementation, results, and a discussion of the strengths and weaknesses of your system. The report should be in HTML or PDF format, and we expect lots of pretty pictures! In addition, please submit your code (or links to sites from which you downloaded pre-trained models, etc.), and links to any datasets you used. If you captured your own data, it is not necessary to submit a full dataset - just include a few samples. Please submit the report here.

Project ideas:

• Set up a webcam in a public space and perform tracking, counting, and/or classification of people, cars, etc.
• Image mosaicing, including automatic image alignment and multiresolution blending.
• Foliage/tourist removal from several photos of a building. An important question to answer is whether you want to attempt 3D reconstruction as part of the process, or whether you want to consider it as a purely 2D problem.
• Video textures - see the SIGGRAPH paper linked from the video textures web page.
• OCR or handwriting recongition.

Project ideas for those with graphics experience:

• Inserting computer-generated objects into a video sequence taken with a moving camera. Use a calibration or structure from motion method to recover the camera pose.
• Some variant of Facade (human-assisted architectural modeling from a small number of photographs). See the the SIGGRAPH 96 paper linked from the Facade web page.
• Vision-based automatic image morphing (e.g., of faces). That is, you use an optical flow or other correspondence method to generate matches between images, then use a morphing algorithm to generate intermediate frames.
• Image-based visual hull (shape from silhouettes) for moving scenes. See the SIGGRAPH 2000 paper, linked from their web page.