A geometric radial basis function network for tracking variant 3D transformations

This paper presents a new hypercomplex valued Radial Basis Network. This network constitutes a generalization of the standard real valued RBF. This geometric RBF can be used in real time to estimate changes in linear transformations in 3D space between sets of geometric entities. Experiments using stereo image sequences validate this proposal. We show the tracking of changes in the orientation between sets of lines and planes. This is a promising approach for neural computing applications in visual guided robotics.