Friday, January 15, 2010

Feature correspondences

One thing I've been trying to do is change the descriptors used in the proposed method to something more modern. The proposed method currently uses Harris corner detector for interest points, and a filter bank as a descriptor. Something more modern would be the SIFT descriptor (stands for Scale Invariant Feature Transform). I've been using the implementation from . A comparison of correspondences can be found here: . These images were manually cropped from random locations in google maps.

One problem is that the proposed method uses "perturbed interest points":

According to the principle of perturbation, a stable system will remain at or near equilibrium even as it is slightly modified. The same holds true for stable matches. To take advantage of this principle, we dilate the interest points to be disks with a radius of rp , where each pixel in the disk is added to the list of interest points. This allows the correct matches to get support from the points surrounding a given feature while incorrect matches will tend to have almost random matches estimated for their immediate neighbors, which will not likely contribute to a widely supported warp.

Which brings up a question, if SIFT is to be used instead of the filter bank descriptor, should the "perturbed" points receive a copy of the original descriptor? The implementation from provides a function for a dense sift descriptor (see ) though the wording on this function is a little cryptic, so I'm not sure if it is the right thing to use.

I ran the code for the proposed method on some of the streetview images I manually cropped, the results can be seen here:

I've been looking into other papers that deal with Google streetview images. In they use a structure from motion structure from motion filter, described here. The UCLA paper is about 3D reconstruction, though the discussion on the SFM filter was interesting. It has been hard to find truly relevant papers though.

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