Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
2d Object Detection and Recognition: Models, Algorithms, and Networks
2d Object Detection and Recognition: Models, Algorithms, and Networks
Hardware-software system for acceleration of image processing operations
Machine Graphics & Vision International Journal
An image-based, trainable symbol recognizer for hand-drawn sketches
Computers and Graphics
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
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
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
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
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
Proceedings of the 32nd DAGM conference on Pattern recognition
Goal evaluation of segmentation algorithms for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
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This paper presents a cascaded system for recognition of the circular road-signs. The system consists of two compound detectors-classifiers. Each operates on the Gaussian scale-space and does template matching in the log-polar domain. The first module is responsible for detection of the potential sign areas at the coarsest level of the pyramid. The second one, in turn, refines the already found places at the finest level. Thanks to this composition, as well as to the efficient matching in the log-polar domain, the system is very robust in terms of recognition of the signs with different scales and rotations, as well as under partial occlusions, poor illumination conditions, and noise.