2d Object Detection and Recognition: Models, Algorithms, and Networks
2d Object Detection and Recognition: Models, Algorithms, and Networks
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
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
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
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
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
Detection and classification of road signs for automatic inventory systems using computer vision
Integrated Computer-Aided Engineering
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The automatic detection and recognition of road signs play important role in the driver assistance systems and can increase the safety on the roads. In this paper we propose a system of a road signs classifier which is based on ensemble of the non Euclidean distance neural networks and an arbitration unit. The input to this system comes from the sign detection module which supplies a normalized, binarized and resampled pictogram of a detected sign. The system performs classification on deformable models. The classifier is composed of a mixture of experts (binary distance neural networks) operating on slightly tilted or shifted versions of pictograms. This ensemble of experts is orchestrated by an arbitration module which operates in the winner-takes-all mode with a novel modification of promoting the most populated group of unanimous experts. The experimental results showed great robustness of the system and very fast response time which is an important factor in the driving assistance systems.