SIFT matches and recognition?

Question

I am developing an application where I am using SIFT + RANSAC and Homography to find an object (OpenCV C++,Java). The problem I am facing is that where there are many outliers R

Answer 1

To complete Dima's , one needs to add that the 4D Hough space is quantized into a (possibly small) number of 4D boxes, where each box corresponds to the simiéarity given by its center.

Then, for each possible similarity obtained via a tentative matching of features, add 1 into the corresponding box (or cell) in the 4D space. The output similarity is given by the cell with the more votes.

In order to computethe transform from 1 match, just use Dima's formulas in his answer. For several pairs of matches, you may need to use some least squares fit.

Finally, the transform can be applied with the function cv::warpPerspective(), where the third line of the perspective matrix is set to [0,0,1].