Hough clustering and sparse matrices
Hi, I would like to implement Hough clustering method described in this
paper.
The whole idea is can be summarized in following way.
- Do typical nearest-neighbour matching between set of features from template and image. Obtaining set SM of matched features.
- Build 4-dimmensional grid parametrized by object position, scale and rotation, each grid element can contain a feature.
- Assign features from SM to grid "points" using feature's internal position, orientation etc.
- Pick the grid point that has the most features in it (represents most probable position).
Any suggestion how could I implement this in fastest and easiest way?
Also can I make sparse matrices in openCV with values being arbitrary classes? Or it has to be numerical type?
"Also can I make sparse matrices in openCV with values being arbitrary classes? "
not really. you can put classes into a Mat, but there has to be a Datatype for it (which is pretty restrictive)