Rotation invariant categorization of visual objects using radon transform and self-organizing modules

Abstract

The Radon transform in combination with self-organizing maps is used to build the rotation invariant systems for categorization of visual objects. The first system has one SOM per the Radon transform direction. The outputs from these directional SOMs that represent positions of the winners and related post-synaptic activities, form the input to the final categorizing SOM. Such a network delivers robust rotation invariant categorization of images rotated by angles up to around 12 o . In the second network the angular Radon transform vectors are combined together and form the input to the categorizing SOM. This network can correctly categorized visual stimuli rotated by up to 30 o . The rotation invariance can be improved by increasing the number of Radon transform angle, which has been equal to six in our initial experiments. © 2010 Springer-Verlag.