First, I'd like to say that I was a bit disappointed with the experimental setup, presentation and analysis of both papers, particularly the Pilu paper. I felt the papers should have done a better job of collecting an interesting set of cases/problems and presenting the results of those. In particular, the Pilu paper suggests that the algorithm presented works fairly well (though he does mention that maybe the method should only be used for bootstrapping), but only on a limited set of examples was tested and the actual analysis of the results from these examples is also minimal.

I was also wondering if anyone else had any better ideas for constructing G than is done in the Pilu paper. I think one of the key things that this paper presents is that we should try and include similarity information between the features when trying to do the matching. What I don't think the paper discussed enough, however, were the options that we have for doing this and the effectiveness of these options. The paper presents the normalized correlation as a one method for representing the similarity, but this is not the only way that this similarity could be presented. Also, the way in which the proximity and similarity portions are combined is fairly simplistic. One could easily consider at a minimum weighting these two things in some manner to get better results. The choices that they made should have been better justified either theoretically or experimentally.