Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Road recognition for vision navigation of an autonomous vehicle by fuzzy reasoning
Fuzzy Sets and Systems
Computer Vision and Image Understanding
A Maximum-Likelihood Strategy for Directing Attention during Visual Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Feature Hierarchical Template Matching Using Distance Transforms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Lane detection by orientation and length discrimination
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The YARF system for vision-based road following
Mathematical and Computer Modelling: An International Journal
Traffic sign recognition using colour information
Mathematical and Computer Modelling: An International Journal
IEEE Transactions on Image Processing
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
Automatic change detection of driving environments in a vision-based driver assistance system
IEEE Transactions on Neural Networks
Adaptive traffic road sign panels text extraction
ISPRA'06 Proceedings of the 5th WSEAS International Conference on Signal Processing, Robotics and Automation
Traffic sign shape classification based on correlation techniques
ISCGAV'05 Proceedings of the 5th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Recognition of mandatory traffic signs using the Hausdorff distance
ISCGAV'05 Proceedings of the 5th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Using self-organising maps in the detection and recognition of road signs
Image and Vision Computing
Traffic sign recognition based on genetic RBFNN
ISC '07 Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Efficient algorithm for automatic road sign recognition and its hardware implementation
Journal of Real-Time Image Processing
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This paper presents an automatic road sign detection and recognition system that is based on a computational model of human visual recognition processing. Road signs are typically placed either by the roadside or above roads. They provide important information for guiding, warning, or regulating the behaviors drivers in order to make driving safer and easier. The proposed recognition system is motivated by human recognition processing. The system consists of three major components: sensory, perceptual, and conceptual analyzers. The sensory analyzer extracts the spatial and temporal information of interest from video sequences. The extracted information then serves as the input stimuli to a spatiotemporal attentional (STA) neural network in the perceptual analyzer. If stimulation continues, focuses of attention will be established in the neural network. Potential features of road signs are then extracted from the image areas corresponding to the focuses of attention. The extracted features are next fed into the conceptual analyzer. The conceptual analyzer is composed of two modules: a category module and an object module. The former uses a configurable adaptive resonance theory (CART) neural network to determine the category of the input stimuli, whereas the later uses a configurable heteroassociative memory (CHAM) neural network to recognize an object in the determined category of objects. The proposed computational model has been used to develop a system for automatically detecting and recognizing road signs from sequences of traffic images. The experimental results revealed both the feasibility of the proposed computational model and the robustness of the developed road sign detection system.