Neural Computation
2d Object Detection and Recognition: Models, Algorithms, and Networks
2d Object Detection and Recognition: Models, Algorithms, and Networks
An image-based, trainable symbol recognizer for hand-drawn sketches
Computers and Graphics
Object detection in multi-channel and multi-scale images based on the structural tensor
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
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
IEEE Transactions on Image Processing
Circular road signs recognition with soft classifiers
Integrated Computer-Aided Engineering - Artificial Neural Networks
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Road Signs Recognition by the Scale-Space Template Matching in the Log-Polar Domain
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
A Real-Time Vision System for Traffic Signs Recognition Invariant to Translation, Rotation and Scale
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Real-time detection of the triangular and rectangular shape road signs
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
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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.