Learning procedure for the recognition of 3-D objects from 2-D images

A learning procedure is described for the recognition of 3d industrial objects from 2d images. It is assumed that the objects are solid and have well defmed edges and that viewpoint and lightning are well defined but that there is no information available on the orientation distribution of future objects to be classified. The presented learning procedure covers all orientations by an initial sampling detects gaps and deletes superfluous orientations. An example is presented.