Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric partial differential equations and image analysis
Geometric partial differential equations and image analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
An Introduction to 3D Computer Vision Techniques and Algorithms
An Introduction to 3D Computer Vision Techniques and Algorithms
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific 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
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
Automatic detection and recognition of signs from natural scenes
IEEE Transactions on Image Processing
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The paper describes a method for automatic scene segmentation and nonlinear shape-preserving filtering for precise detection of road signs in real traffic scenes. Segmentation is done in the RGB color space with a version of the fuzzy k-means method. The obtained posterior probabilities are then nonlinearly filtered with the novel version of the shape-preserving anisotropic diffusion. In effect more precise detection of object boundaries is possible. Thanks to this, the overall quality of the detection stage was increased, as it was confirmed by many experiments.