Neural Networks
Practical neural network recipes in C++
Practical neural network recipes in C++
Affine moment invariants: a new tool for character recognition
Pattern Recognition Letters
2d Object Detection and Recognition: Models, Algorithms, and Networks
2d Object Detection and Recognition: Models, Algorithms, and Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Rotation invariant recognition of road signs with ensemble of 1-NN neural classifiers
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Computational framework for family of order statistic filters for tensor valued data
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Recognition of road signs with mixture of neural networks and arbitration modules
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Automatic detection and recognition of signs from natural scenes
IEEE Transactions on Image Processing
Circular road signs recognition with soft classifiers
Integrated Computer-Aided Engineering - Artificial Neural Networks
Intelligent System for Traffic Signs Recognition in Moving Vehicles
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
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In this paper the neural classifier for recognition of the circular shaped road signs is presented. This classifier belongs to the road signs recognition module, which in turn is a part of a driver assisting system. The circular shaped prohibition and obligation signs constitute the very important groups within the set of road signs. In this case however, it is not possible for a detector to determine rotation of the shapes that would allow dimension reduction of the search space. Thus the classifier has to be able to properly work with all possible affine deformations. To alleviate this problem we propose to use as features the statistical moments which were shown to be invariant within an affine group of transformations. The classification is performed by the probabilistic neural network which is trained with sign examples extracted from the real traffic scenes. The obtained results show good accuracy of classification and fast operation time.