Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Road sign classification using Laplace kernel classifier
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning (Synthesis Lectures on Image, Video, and Multimedia Processing)
Circular road signs recognition with soft classifiers
Integrated Computer-Aided Engineering - Artificial Neural Networks
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
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Differentiation of discrete multidimensional signals
IEEE Transactions on Image Processing
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In this paper a system is presented for real time recognition of the traffic signs. Sign detection is done by a method of adaptively growing window. Classification is based on matching of the modulo-shifted phase histograms. These are built from the stick component of the structural tensor rather than from an edge detector. To cope with inherent rotations of signs a novel measure is proposed for matching of the modulo-shifted histograms that also boosts responses of highly probable values. The method is tolerant of small translations, rotations and symmetrical changes of scale. It works also well under different lighting conditions and tolerates noise and small occlusions.