Top 5415 PC Vision Fall 2004.


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Top 5415 PC Vision Fall 2004. Dr. Alper Yilmaz Univ. of Focal Florida www.cs.ucf.edu/courses/cap5415/fall2004 Office: CSB 250. Recap Circle Fitting. Circle comparison Change x 0 , and y 0 figure r Change just r and process ( x 0 , y 0 ) from picture inclination edge 
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Top 5415 Computer Vision Fall 2004 Dr. Alper Yilmaz Univ. of Central Florida www.cs.ucf.edu/courses/cap5415/fall2004 Office: CSB 250 Alper Yilmaz, Fall 2004 UCF

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Recap Circle Fitting Circle mathematical statement Change x 0 , and y 0 figure r Change just r and process ( x 0 , y 0 ) from picture slope edge  Increment ( x 0 , y 0 , r ) in collector exhibit Find the nearby maxima Alper Yilmaz, Fall 2004 UCF

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Recap Generalized Hough Transform For shapes with no explanatory expression Requires learning of r-table Distance vector and its plot for every limit pixel to protest centroid Finding a shape in a picture For information edge-guide register edge (can be from picture inclination) For every separation vector comparing to edge augment a 2D aggregator cluster relating to ( x 0 , y 0 ) Alper Yilmaz, Fall 2004 UCF

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Recap Medial Axis Transform Represents article shape: skeleton of an item Computed utilizing an iterative calculation, Inverse change exist Alper Yilmaz, Fall 2004 UCF

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Recap Interest Points High surface variety in neighborhood of a pixel Movarec’s administrator Compute directional force varieties in 4x4 neighborhood Pick focuses with high power varieties in an area Harris corner locator Compute a minute lattice M from angles in an area Min eigenvalue of M higher than an edge demonstrates corner Alper Yilmaz, Fall 2004 UCF

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Object Motion Alper Yilmaz, Fall 2004 UCF

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Motion Projection of item movement in true (3D) results in movement in picture plane (2D) 2D movement is characterized over a succession of casings Computed by utilizing shine steadiness limitation Intensity of a moving pixel does not change after some time Alper Yilmaz, Fall 2004 UCF

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What is its utilization? Bunches of employments Motion Detection Track object conduct Correct for camera jitter (adjustment) Align pictures (mosaics) 3D shape recreation Video Compression Alper Yilmaz, Fall 2004 UCF

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Measurement of movement at each pixel Alper Yilmaz, Fall 2004 UCF

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Measurement of movement at each pixel Alper Yilmaz, Fall 2004 UCF

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Visual Mosaics Alper Yilmaz, Fall 2004 UCF

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Visual Mosaics Alper Yilmaz, Fall 2004 UCF

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Geo Registration Alper Yilmaz, Fall 2004 UCF

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Video Segmentation Alper Yilmaz, Fall 2004 UCF

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Structure From Motion Alper Yilmaz, Fall 2004 UCF

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Optical Flow Alper Yilmaz, Fall 2004 UCF

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Optical Flow vector in picture space (2D) Taylor arrangement extension of right side around t Alper Yilmaz, Fall 2004 UCF

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Optical Flow Brightness consistency mathematical statement Equation of a line in ( u , v ) space Alper Yilmaz, Fall 2004 UCF

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Optical Flow We know I x , I y , and I t from pictures For each point these qualities give one comparison We have 2 questions: u , v Solution lie anyplace hanging in the balance v u Alper Yilmaz, Fall 2004 UCF

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d . Optical Flow v p Let ( u’ , v’ ) be genuine stream True stream has two parts Normal stream: d Parallel stream: p Normal stream can be registered Parallel stream can\'t u Alper Yilmaz, Fall 2004 UCF

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Computing True Flow Horn & Schunck Schunk Lukas & Kanade Alper Yilmaz, Fall 2004 UCF

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Horn & Schunck Define a vitality capacity and minimize Differentiate w.r.t. questions u and v laplacian of u laplacian of v Alper Yilmaz, Fall 2004 UCF

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Horn & Schunck Laplacian controls smoothness of optical stream A specific decision can be  2 u = u - u avg ,  2 v = v - v avg . Improving mathematical statements 2 comparisons 2 questions Write v as far as u Plug it in the other mathematical statement Alper Yilmaz, Fall 2004 UCF

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Horn & Schunck Iteratively figure u and v Assume at first u and v are 0 Compute u avg and v avg in an area Alper Yilmaz, Fall 2004 UCF

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Schunck If two neighboring pixels move with same speed Corresponding stream comparisons meet at a point in (u,v) space Find the crossing point purpose of lines If more than 1 convergence focuses discover bunches Biggest group is genuine stream v u Alper Yilmaz, Fall 2004 UCF

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Lucas & Kanade Similar to line fitting we have seen Define a vitality utilitarian Take subordinates liken it to 0 Rearrange and develop a perception grid Alper Yilmaz, Fall 2004 UCF

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Lucas & Kanade 1 Alper Yilmaz, Fall 2004 UCF

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Lucas & Kanade 2 Alper Yilmaz, Fall 2004 UCF

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Discussion Horn-Schunck and Lucas-Kanade optical strategy lives up to expectations just for little movement. On the off chance that protest moves speedier, the brilliance changes quickly, subordinate covers neglect to assess spatiotemporal subsidiaries. Pyramids can be utilized to register extensive optical stream vectors. Alper Yilmaz, Fall 2004 UCF

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u=1.25 pixels u=2.5 pixels u=5 pixels u=10 pixels picture I t picture I t+1 Gaussian pyramid of picture I t Gaussian pyramid of picture I t+1 Coarse to Fine Optical Flow Estimation Alper Yilmaz, Fall 2004 UCF

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Alper Yilmaz, Fall 2004 UCF

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Alper Yilmaz, Fall 2004 UCF

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Alper Yilmaz, Fall 2004 UCF

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Alper Yilmaz, Fall 2004 UCF .:tsli

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