Self-Organizing Maps
Detection of Linear Features using a Localized Radon Transform with a Wavelet Filter
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Radon representation-based feature descriptor for texture classification
IEEE Transactions on Image Processing
Feedback in multimodal self-organizing networks enhances perception of corrupted stimuli
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms
IEEE Transactions on Image Processing
Identification of Reflected, Scaled, Translated, and Rotated Objects From Their Radon Projections
IEEE Transactions on Image Processing
Incremental self-organizing map (iSOM) in categorization of visual objects
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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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°. 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°. The rotation invariance can be improved by increasing the number of Radon transform angle, which has been equal to six in our initial experiments.