Example Results of "HALF-SIFT" (CVPR09)

HALF-SIFT uses the SIFT [1] framework for detecting scale invariant feature points in an image and establishing correspondences between images. By replacing the subpel interpolation scheme of the original SIFT by a regression with an image signal adapted function, the accuracy of the localization of detected points is increased by up to 16% for natural image pairs. Two proposals are made: Results (error histograms of detected corresponding feature points) are shown below

Image Pairs:

Images SIFT vs. HALF-SIFT - DoG SIFT vs. HALF-SIFT - Gaussian Download tif images (1920x1080)
cars A: constant_intrinsics
cars B: auto-iris
cars C: automatic_gain
towers A: constant_intrinsics
towers B: auto-iris
towers C: automatic_gain
gift A: constant_intrinsics
gift B: auto-iris
gift C: automatic_gain
ubc: img1 - img2
ubc: img1 - img3
ubc: img1 - img4