Rotation Invariant Recognition of Road Signs with Ensemble of 1-NN Neural Classifiers

The paper presents a parallel system of two compound classifiers for recognition of the circular shape road signs. Each of the two classifiers is built of an ensemble of 1-nearest-neighbour (1-NN) classifiers and the arbitration unit operating in the winner-takes-all mode. For the 1-NN we employed the Hamming neural network (HNN) which accepts the binary input. Each HNN is responsible for classification within a single group of deformable prototypes of the road signs. Each of the two compound classifiers has the same structure, however they accept features from different domains: the spatial and the log-polar spaces. The former has an ability of precise classification for shifted but non-rotated objects. The latter exhibits good abilities to register the rotated shapes and also to reject the non road sign objects due to its high false negative detection properties. The combination of the two outperformed each of the single versions what was verified experimentally. The system is characterized by fast learning and recognition rates.