Neural computation methods for the point correspondence problem

Summary form only given. Three neural computation methods for helping to overcome the point correspondence problem in the computer vision field are discussed. The first is for two-dimensional correspondence between a model´s points and the input points assumed to have been transformed from the model´s points by an unknown affine transformation. The second is for correspondence between the model points on a three-dimensional object and the input points perspectively projected on a two-dimensional plane from the model points after an unknown motion of the object. The third includes a Boltzmann machine