Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bascón, S.M., Rodríguez, J.A., Arroyo, S.L., Caballero, A.F., L”opez-Ferreras, F.: An optimization on pictogram identification for the road-sign recognition task using SVMs. Computer Vision and Image Understanding 114(3), 373–383 (2010)
Chen, X., Yang, J., Zhang, J., Waibel, A.: Automatic Detection and Recognition of Signs from Natural Scenes. IEEE Trans. on Image Proc. 13(1), 87–99 (2004)
Cyganek, B.: Soft System for Road Sign Detection. In: Theory and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol. 41, pp. 316–326. Springer, Heidelberg (2007)
Cyganek, B.: Real-Time Detection of the Triangular and Rectangular Shape Road Signs. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2007. LNCS, vol. 4678, pp. 744–755. Springer, Heidelberg (2007)
Cyganek, B.: Color Image Segmentation With Support Vector Machines: Applications To Road Signs Detection. International Journal of Neural Systems 18(4), 339–345 (2008)
Cyganek, B., Siebert, J.P.: An Introduction to 3D Computer Vision Techniques and Algorithms. Wiley, Chichester (2009)
de la Escalera, A., Armingol, J.A.: Visual Sign Information Extraction and Identification by Deformable Models. IEEE Transactions On Intelligent Transportation Systems 5(2), 57–68 (2004)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2001)
Gan, G., Ma, C., Wu, J.: Data Clustering. Theory, Algorithms, and Applications. SIAM, Philadelphia (2007)
Kuncheva, L.: Combining Pattern Classifiers. In: Methods and Algorithms, Wiley, Chichester (2004)
Paclík, P., Novovičová, J., Duin, R.P.W.: Building road sign classifiers using a trainable similarity measure. IEEE Transactions on Intelligent Transportation Systems 7(3), 309–321 (2006)
Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. On Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C. The Art of Scientific Computing, 3rd edn. Cambridge University Press, Cambridge (2007)
Sapiro, G.: Geometric Partial Differential Equations and Image Analysis, Cambridge (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cyganek, B. (2010). Traffic Scene Segmentation and Robust Filtering for Road Signs Recognition. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15910-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-15910-7_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15909-1
Online ISBN: 978-3-642-15910-7
eBook Packages: Computer ScienceComputer Science (R0)