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Unformatted text preview: 1 Motion Estimation Optical flow Measurement of motion at every pixel 2 Problem definition: optical flow How to estimate pixel motion from image H to image I? Solve pixel correspondence problem given a pixel in H, look for nearby pixels of the same color in I Key assumptions color constancy: a point in H looks the same in I For grayscale images, this is brightness constancy small motion : points do not move very far This is called the optical flow problem Optical flow equation 3 LukasKanade flow Prob: we have more equations than unknowns The summations are over all pixels in the K x K window This technique was first proposed by Lukas & Kanade (1981) described in Trucco & Verri reading Solution: solve least squares problem minimum least squares solution given by solution (in d) of: Iterative Refinement Iterative LukasKanade Algorithm 1. Estimate velocity at each pixel by solving LucasKanade equations 2. Warp H towards I using the estimated flow field use image warping techniques 3. Repeat until convergence 4 image I image H Gaussian pyramid of image H Gaussian pyramid of image I image I image H u=10 pixels u=5 pixels u=2.5 pixels u=1.25 pixels Coarsetofine optical flow estimation image I image J Gaussian pyramid of image H Gaussian pyramid of image I image I image H Coarsetofine optical flow estimation run iterative LK run iterative LK warp & upsample . . . 5 Multiresolution Lucas Kanade Algorithm Compute Iterative LK at highest level For Each Level i Take flow u ( i1), v ( i1) from level i1 Upsample the flow to create u *( i ), v *( i ) matrices of twice resolution for level i . Multiply u *( i ), v *( i ) by 2 Compute I t from a block displaced by u *( i ), v *( i ) Apply LK to get u ( i ), v ( i ) (the correction in flow) Add corrections u ( i ), v ( i ) to obtain the flow u ( i ), v ( i ) at i th level, i.e., u ( i...
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This note was uploaded on 06/12/2011 for the course CAP 5415 taught by Professor Staff during the Fall '08 term at University of Central Florida.
 Fall '08
 Staff

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