Invariant object recognition based on the generalized discrete radon transform
Glenn R. Easley, Flavia Colonna · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.