1	Announcements Project 3 code & artifact due Tuesday Final project proposals due noon Wed (by email) One-page writeup (from project web page), specifying: Your team members Project goals. Be specific. Describe the input and output. Brief description of your approach. If you are implementing or extending a previous method, give the reference and web link to the paper. Will you be using helper code (e.g., available online) or will you implement it all yourself? Evaluation method. How will you test it? Which test cases will you use? Breakdown--what will each team-member do? Ideally, everyone should do something imaging/vision related (it's not good for one team member to focus purely on user-interface, for instance). Special equipment that will be needed. We may be able to help with cameras, tripods, etc.
2	Stereo Readings Szeliski, Chapter 10 (through 10.5)
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8	Anaglyphs online I used to maintain of list of sites, but too hard to keep up to date. Instead, see wikipedia page: http://en.wikipedia.org/wiki/Anaglyph_image
9	Stereo
10	Stereo
11	Stereo correspondence Determine Pixel Correspondence Pairs of points that correspond to same scene point
12	Fundamental matrix Let p be a point in left image, p’ in right image Epipolar relation p maps to epipolar line l’ p’ maps to epipolar line l Epipolar mapping described by a 3x3 matrix F It follows that
13	Fundamental matrix This matrix F is called the “Essential Matrix” when image intrinsic parameters are known the “Fundamental Matrix” more generally (uncalibrated case) Can solve for F from point correspondences Each (p, p’) pair gives one linear equation in entries of F 8 points give enough to solve for F (8-point algorithm) see Marc Pollefey’s notes for a nice tutorial
14	Stereo image rectification
15	Stereo image rectification reproject image planes onto a common plane parallel to the line between optical centers pixel motion is horizontal after this transformation two homographies (3x3 transform), one for each input image reprojection C. Loop and Z. Zhang. Computing Rectifying Homographies for Stereo Vision. IEEE Conf. Computer Vision and Pattern Recognition, 1999.
16	Stereo matching algorithms Match Pixels in Conjugate Epipolar Lines Assume brightness constancy This is a tough problem Numerous approaches A good survey and evaluation: http://www.middlebury.edu/stereo/
17	Your basic stereo algorithm
18	Window size Smaller window Larger window
19	Stereo results Data from University of Tsukuba Similar results on other images without ground truth
20	Results with window search
21	Better methods exist...
22	Stereo as energy minimization
23	Stereo as energy minimization
24	Depth from disparity
25	Video View Interpolation http://research.microsoft.com/users/larryz/videoviewinterpolation.htm
26	Real-time stereo Used for robot navigation (and other tasks) Several software-based real-time stereo techniques have been developed (most based on simple discrete search)
27	Stereo reconstruction pipeline Steps Calibrate cameras Rectify images Compute disparity Estimate depth
28	Active stereo with structured light Project “structured” light patterns onto the object simplifies the correspondence problem
29	Active stereo with structured light
30	Laser scanning Optical triangulation Project a single stripe of laser light Scan it across the surface of the object This is a very precise version of structured light scanning
31	Laser scanned models
32	Laser scanned models
33	Laser scanned models
34	Laser scanned models
35	Laser scanned models
36	Spacetime Stereo