Practical methods of optimization; (2nd ed.)
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Numerical recipes in C (2nd ed.): the art of scientific computing
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Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
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Digital Image Processing
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
2d Object Detection and Recognition: Models, Algorithms, and Networks
2d Object Detection and Recognition: Models, Algorithms, and Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Data Description
Machine Learning
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Road Signs Recognition by the Scale-Space Template Matching in the Log-Polar Domain
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
An image-based, trainable symbol recognizer for hand-drawn sketches
Computers and Graphics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Building Road-Sign Classifiers Using a Trainable Similarity Measure
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Image Processing
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
Real-Time Road Signs Tracking with the Fuzzy Continuously Adaptive Mean Shift Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Neuro-fuzzy System for Road Signs Recognition
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
A Real-Time Vision System for Traffic Signs Recognition Invariant to Translation, Rotation and Scale
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Using self-organising maps in the detection and recognition of road signs
Image and Vision Computing
An optimization on pictogram identification for the road-sign recognition task using SVMs
Computer Vision and Image Understanding
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
Road-signs recognition system for intelligent vehicles
RobVis'08 Proceedings of the 2nd international conference on Robot vision
An incremental-encoding evolutionary algorithm for color reduction in images
Integrated Computer-Aided Engineering
Mixed color/level lines and their stereo- matching with a modified Hausdorff distance
Integrated Computer-Aided Engineering
Visual system for drivers' eye recognition
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Object recognition with the HOSVD of the multi-model space-variant pattern tensors
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Neural network based smart vision system for driver assistance in extracting traffic signposts
Proceedings of the CUBE International Information Technology Conference
Detection and classification of road signs for automatic inventory systems using computer vision
Integrated Computer-Aided Engineering
Lane mark segmentation and identification using statistical criteria on compressed video
Integrated Computer-Aided Engineering
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In this paper a computer system for recognition of the circular prohibition and obligation road signs is proposed. Its main purpose is to assist a driver with information on passing signs, which in connection with an active cruise control can prevent dangerous traffic situations. Thus, the system can help to increase safety on our roads. The proposed system consists of two main parts: a detector and a classification module. Both employ soft classifiers. The detector does colour segmentation with a support vector machine, operating in a one-class mode. Then the circular shapes are found and passed to the classifiers. The classification module is built in a form of two committee machines, each composed of a series of expert neural networks and an arbitration unit. The two machines has the same internal structure, however they operate in different input spaces. The first one works in the spatial domain, which allows very accurate assessments of the relative vertical and horizontal shifts. The second machine operates in the log-polar representation which has the ability to represent rotations as vertical shifts. Each expert of a committee machine is realized as a Hamming neural network trained with affinely deformed set of reference road signs from the data base. Selection of a single answer from a group of experts is done by an arbitration unit which operates in the winner-takes-all mode. Additionally, arbitration has been endowed with a group support mechanism which boosts answers from a group of unanimous experts. The proposed system shows very accurate and fast response on circular road signs encountered in real traffic scenes. This has been verified by experiments which results are also presented and discussed in this paper.