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CAP 5415 Computer Vision Fall 2004

CAP 5415 Computer Vision Fall 2004. Dr. Alper Yilmaz Univ. of Central Florida www.cs.ucf.edu/courses/cap5415/fall2004 Office: CSB 250. Recap Epipolar Geometry. Essential matrix Fundamental matrix. P. x l. P l. x r. P r. C l. C r. epipole. epipole. Recap 8-Point Algorithm.

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CAP 5415 Computer Vision Fall 2004

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  1. CAP 5415 Computer VisionFall 2004 Dr. Alper Yilmaz Univ. of Central Florida www.cs.ucf.edu/courses/cap5415/fall2004 Office: CSB 250 Alper Yilmaz, Fall 2004 UCF

  2. RecapEpipolar Geometry • Essential matrix • Fundamental matrix P xl Pl xr Pr Cl Cr epipole epipole Alper Yilmaz, Fall 2004 UCF

  3. Recap8-Point Algorithm • N≥8 corresponding points in both images • Normalize and scale coordinates • Construct an observation matrix • Compute OTO and its eigenspace decomposition • Select min eigenvalued eigenvector as solution • Enforce rank 2 constraint • Compute SVD and set smallest singular value to 0 Alper Yilmaz, Fall 2004 UCF

  4. J. Shi and J. Malik, “Normalized Cuts & Image Segmentation,” IEEE Trans. of PAMI, Aug 2000. RecapGraph Cuts for Image Segmentation • Let all pixels be nodes of a graph • Compute similarity between each node to all other nodes of graph • Construct weight matrix W and diagonal cost matrix D • Compute eigenvector of (D-W) • Use the eigenvector with second smallest eigenvalue to segment image to 2 segments • Iteratively do these steps Alper Yilmaz, Fall 2004 UCF

  5. RecapDrawbacks of Minimum Normalized Cut • Huge storage requirement • Takes a lot of time • Bias towards partitioning into equally sized segments Alper Yilmaz, Fall 2004 UCF

  6. Mid Term 2 - 24 November 2004 • Hough Transform • Line, circle fitting • Generalized Hough transform • Interest point, corner detectors • Pixel based optical flow • Token based optical flow • Global motion • Shape from motion • Geometry of a stereo camera pair • Stereopsis • Fundamental matrix (estimation) • Graph based image segmentation Alper Yilmaz, Fall 2004 UCF

  7. Programming AssignmentDue date 3 December 2004 • Implement Lucas&Kanade method for computing optical flow. • Build Gaussian pyramid • Start from lowest resolution compute optical flow for each pixel • Go to next pyramid level by projecting computed optical flows • You need to replicate optical flow for erased pixels • Warp image intensities using the optical flow vector • Display flow vectors using quiver plot (plot every 5 pixel as a vector) • Deliverables • Report including all pyramid level images and flow vectors of each pyramid level along with source code Alper Yilmaz, Fall 2004 UCF

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