Fundamentals of digital image processing
Fundamentals of digital image processing
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Road Signs Recognition Using a Dynamic Pixel Aggregation Technique in the HSV Color Space
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Robust artificial landmark recognition using polar histograms
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Shape classification algorithm using support vector machines for traffic sign recognition
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
An optimization on pictogram identification for the road-sign recognition task using SVMs
Computer Vision and Image Understanding
Goal evaluation of segmentation algorithms for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
Real-time Korean traffic sign detection and recognition
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Journal of Visual Communication and Image Representation
| Hi-index | 0.09 |
The main goal of a traffic sign recognition system is the detection and recognition of every traffic sign present in the scene. Frequently, the image processing system is divided into three parts, namely, segmentation, detection and recognition. In this work, we will focus on the detection block, dividing it into two sub-blocks that perform shape classification and localization of the sign, respectively. The classification of the shape is performed by means of the signature of the connected components. Object rotations are tackled with the use of the FFT, and the normalization of the object eccentricity improves the performance in the presence of projection distortions. The effect of occlusions are lowered removing the concave parts of the shape. Finally, we propose a novel algorithm, which computes a 2D homography, to re-orientate the sign for further steps, like sign recognition. Experimental results, evaluated using a huge set of randomly generated synthetic images are also given, showing a great robustness of the algorithm to object scaling, rotation, projective deformation, partial occlusions and noise.