Moderator: Computer Vision
So even if I
1) choose a pretty conservative guess of the inlier probablity (-> a rather big number of iterations),
2) throw in a couple of different thresholds (doesn't matter that much) and
3) do only accept homographies up to a certain condition number (because samples may be ill-posed)
the resulting stitch is not always as appealing as with the given solution in 'H.mat'. Is there a certain trick to always get good results? Or can we expect RANSAC fail to deliver a good solution every now and then?
BTW, a tip for visual debugging: I found it quite nice to draw different lines for different correspondences so that distinguishing is more easy...
You can do that by defining a colormap for the correspondence lines...
Code: Alles auswählen
% Different colors for different lines cmap = hsv(numPairs); % random index s.t. neighboring lines may have different colors ridx = randperm(numPairs); for i = 1:size(pairs, 1) currPoints = ... plot(currPoints, 'color', cmap(ridx(i), :)); end
- colored.jpg (43.5 KiB) 692 mal betrachtet