Computational strategies for object recognition
ACM Computing Surveys (CSUR)
Real-time classification of traffic signs
Real-Time Imaging
Road sign classification using Laplace kernel classifier
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Fast and Robust Segmentation of Natural Color Scenes
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Traffic Sign Recognition Revisited
Mustererkennung 1999, 21. DAGM-Symposium
Computer Vision and Image Understanding - Special issue on event detection in video
Gabor wavelet similarity maps for optimising hierarchical road sign classifiers
Pattern Recognition Letters
Scale and skew-invariant road sign recognition
International Journal of Imaging Systems and Technology
Circular road signs recognition with soft classifiers
Integrated Computer-Aided Engineering - Artificial Neural Networks
Detection and classification of road signs in natural environments
Neural Computing and Applications
A hybrid system for embedded machine vision using FPGAs and neural networks
Machine Vision and Applications
Traffic sign recognition using colour information
Mathematical and Computer Modelling: An International Journal
Dynamic background modeling for a safe road design
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Clustering Indian stock market data for portfolio management
Expert Systems with Applications: An International Journal
Real-Time GPU based road sign detection and classification
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Efficient algorithm for automatic road sign recognition and its hardware implementation
Journal of Real-Time Image Processing
| Hi-index | 0.00 |
Road sign recognition is a part of driver support systems. Its main aim is the increase of traffic safety by calling the driver's attention to the presence of key traffic signs. Additionally, a vision-based system able to detect and classify traffic signs from road images in real-time would also be useful as a support tool for guidance and navigation of intelligent vehicles. This paper proposes a new method for the detection and recognition of traffic signs using self-organising maps (SOM). This method first detects potential road signs by analysing the distribution of red pixels within the image, and then identifies these road signs from the distribution of dark pixels in their pictograms. Additionally, a novel hybrid system combining programmable hardware and artificial neural networks for embedded machine vision is introduced, and a prototype of this system is used in the implementation of the application. The experiments indicate a good performance of the new approach using SOM in both speed and classification accuracy.