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# Lecture-7 - Lecture-7 Feature-based Registration Steps in...

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1 Lecture-7 Feature-based Registration Steps in Feature-based Registration Find features Establish correspondence (correlation, point correspondence) Fit transformation Apply transformation (warp)

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2 Features Any pixels Corner points Interest points Features obtained using Gabor/Wavelet filters Straight lines Line intersections Transformations • Affine • Projective • Psuedo-perspective Rational polynomial
3 Good Features to Track • Corner like features • Moravec’s Interest Operator Corner like features C = f x 2 Q f x f y Q f x f y Q f y 2 Q = 2 1 0 0 l l C

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4 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 = = l l 0 , 0 1 2 > = l l 0 2 1 f l l Algorithm Corners Compute the image gradient 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.
5 Algorithm Corners Sort L in decreasing order of eigenvalues. 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

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6 Results
7 Moravec’s Interest Operator Algorithm • Compute four directional variances in horizontal, vertical, diagonal and anti- diagonal directions for each 4 by 4 window.

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