UD CISC 689 - Going Back a little (38 pages)

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Going Back a little



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Going Back a little

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Pages:
38
School:
University of Delaware
Course:
Cisc 689 - Topics: Artificial Intelligence: MACHINE LEARNING

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Going Back a little Cameras ppt Computer Vision CISC 4 689 Applications of RANSAC Solution for affine parameters Affine transform of x y to u v Rewrite to solve for transform parameters Computer Vision CISC 4 689 Assignment Program 1 info Link Data te u can generate bring in your own images from www as long as r n 1 levels image must be Mr 2n 1 rows by Mc 2n 1 cols and Mc are any ve integers Sunday 10pm Computer Vision CISC 4 689 Another app Automatic Homography H Estimation Homographies describe image transformation of General scene when camera motion is rotation about camera center Planar surfaces under general camera motion How to get correct correspondences without human intervention from Hartley Zisserman Computer Vision CISC 4 689 Computing a Homography Lets Side track 8 degrees of freedom in 3 x 3 matrix H so at least n points are sufficient to determine it Use same basic algorithm for P aka Direct Linear Transformation or DLT 4 pairs of 2 D to compute H Now stacked matrix A is 2n x 9 vs 2n x 12 for camera matrix P estimation because all points are 2 D 3 collinear points in either image is a degenerate configuration preventing a unique solution Computer Vision CISC 4 689 Estimating H DLT Algorithm x 0i Hx i is an equation involving homogeneous vectors so Hx i and x 0i need only be in the same direction not strictly equal We can specify same directionality by using a cross product formulation See Hartley Zisserman Chapter 3 1 3 1 1 linked on course page for details Computer Vision CISC 4 689 Texture Mapping Needed for nice display when applying transformations like a homography H to a whole image Simple approach Iterate over source image coordinates and apply x 0 get destination pixel location H x to Problem Some destination pixels may not be hit leaving holes Easy solution Iterate over destination image and apply inverse transform x 1 x0 Round off H 1 x 0 to address nearest source pixel value This ensures every destination pixel is filled in Computer Vision



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