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# Lecture-8 - Steps in Feature-based Registration Lecture-8...

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1 Lecture-8 Feature-based Registration Steps in Feature-based Registration ! Find features ! Establish correspondences between features in two images (correlation, point correspondence) ! Fit transformation ! Apply transformation (warp) Features ! All pixels (spatiotemporal approach) ! Corner points ! Interest points ! Straight lines ! Line intersections ! Features obtained using Gabor/Wavelet filters ! Transformations ! Affine ! Projective ! Psuedo-perspective ! Rational polynomial Good Features to Track ! Corner like features ! Moravec’s Interest Operator Corner like features ! ! ! " # \$ \$ \$ % & ( ( ( ( Q y Q y x Q y x Q x f f f f f f C 2 2 ! " # \$ % & 2 1 0 0 ) ) D Q is an image patch Eigen Values

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2 Corners For perfectly uniform region If Q contains an ideal step edge, then if Q contains a corner of black square on white background 0 2 1 ) ) 0 , 0 1 2 * ) ) 0 2 1 ! ) ) + Algorithm Corners ! Compute the image gradient ( f x , f y ) over entire image f. ! For each image point p: " form the matrix C over (2N+1)X(2N+1) neighborhood Q of p; " compute the smallest eigenvalue of C; " if eigenvalue is above some threshold, save the coordinates of p into a list L. Algorithm Corners ! Sort L in decreasing order of eigenvalues. ! Select the top candidate corner, and perform Non-maxima suppression " Scanning the sorted list top to bottom: for each current point, p, delete all other points on the list which belong to the neighborhood of p. Results Results
3 Moravec’s Interest Operator Algorithm !

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