文件列表:

siftfeature\it_features_matching.m (3500, 2005-05-02)
siftfeature\circ_corr_2d.m (1535, 2005-05-02)
siftfeature\features_matching.m (3663, 2005-05-02)
siftfeature\circ_corr_1d.m (1463, 2005-05-02)
siftfeature\temp_temp_matching.m (3318, 2005-05-02)
siftfeature\decimage.m (141, 2004-05-04)
siftfeature (0, 2010-09-03)

The function produce a set of features matched through a pair of views. The selection is driven by the estimate uncertainty extracted from the Fisher information matrix. The main script if features_matching.m Usage: [x1,x2,corr_vec]=features_matching(im1, im2, cal, initsplit, sm_sigma, sigma_max) Input im1, im2 : stereo pair. greyscale images cal : (1=rectified images with epipolar horizontal lines; 0=uncalibrated images ) initsplit : images initial splitting factor; the images are splitted in (initsplit x initsplit) subimages. sm_sigma : smoothing Gaussian filter SD (1 -:- 3 for normal smoothing) sigma_max : Fisher index th (1e-2 -:- 1e-4) Output x1, x2 : matched points in the left and right images; 2 x N matrices x1=[u1 u2 ....uN horiz. coord. v1 v2 ....vN] vert. coord. corr_vec : match strength Comments to: Lorenzo Sorgi Virtual Reality lab Italian Aerospace Research Centre l.sorgi@cira.it

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