Three-Dimensional Object Representation and Invariant Recognition Using Continuous Distance Transform Neural Networks

Invariant 3-D object recognition under partial or noisy object viewing is a difficult pattern recognition task. In this paper, we introduce a new neural network solution that is robust to noise corruption and partial viewing of objects. This method directly utilizes the acquired 3-D data and requires no feature extraction. In the proposed approach, based on the training surface points of the exemplar object, the object is first parametrically represented by a continuous distance transform neural network (CDTNN) which maps any 3-D coordinate into its corresponding distance value between the point to the nearest surface point of the object. When later presented with the surface points of an unknown partial viewing object with deformation (e.g., rotation and translation of rigid body objects or irregular deformation of deformable objects), the CDTNN representation allows gradually determining the best deformation (affine transform and/or translation/rotation) of the unknown object. The mi..