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Hi, I would like to implement Hough clustering method described in this
paper.

The whole idea is can be summarized in following way.

1. Do typical nearest-neighbour matching obtains Set S of matched features.
2. Build 4-dimmensional grid parametrized by object position, scale and rotation, each grid element can contain a feature.
3. Assign features to grid using feature internal positin, orientation etc.
4. Pick the bins with most feature

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?

Hi, I would like to implement Hough clustering method described in this
paper.

The whole idea is can be summarized in following way.

1. Do typical nearest-neighbour nearest-neighbour matching between set of features from template and image. Obtaining set obtains Set S SM of matched matched features.
2. Build 4-dimmensional grid parametrized by object position, scale and rotation, each grid element can contain a feature.
3. Assign features from SM to grid "points" using feature feature's internal positin, position, orientation etc.
4. Pick the bins with grid point that has the most featurefeatures 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?

Hi, I would like to implement Hough clustering method described in this
paper.

The whole idea is can be summarized in following way.

1. Do typical nearest-neighbour matching between set of features from template and image. Obtaining set SM of matched features.
2. Build 4-dimmensional grid parametrized by object position, scale and rotation, each grid element can contain a feature.
3. Assign features from SM to grid "points" using feature's internal position, orientation etc.
4. 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 matrices in openCV with values being arbitrary classes? Or it has to be numerical type?

Hi, I would like to implement Hough clustering method described in this
paper.

The whole idea is can be summarized in following way.

1. Do typical nearest-neighbour matching between set of features from template and image. Obtaining set SM of matched features.
2. Build 4-dimmensional grid parametrized by object position, scale and rotation, each grid element can contain a feature.
3. Assign features from SM to grid "points" using feature's internal position, orientation etc.
4. 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?