Author, Subjects, Keywords

Cited Author

 
 
   » By Author or Editor
 » Browse Author by Alphabet
 » By Journal
 » By Subjects
 » Malaysian Journals
 » By Type
 » By Year
 » By Latest Additions
 
 
   » By Author
 » Top 20 Authors
 » Top 20 Article
 » Top Journal Cited
 » Top Article Cited
 » Journal Citation Statistics
 » Usage Since Sept 2007


 
 
 

Login | Create Account

Road sign recognition using affine moment invariant*

Choong, Yeun Liong, (2004) Road sign recognition using affine moment invariant*. Journal of ICT, 3 (2). pp. 59-76. ISSN 1675414X

[img]PDF (Corrected version) - Registered users only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
18Kb

Affiliations

Universiti Kebangsaan Malaysia. Faculty of Science and Technology. School of Mathematical Sciences

Abstract

Ability to recognise road sign in due time is an essential aspect of safe driving especially at night time. Hence an auto road sign recognition system is a desirable add-on to a night vision system. This paper presents the use of affine moment invariants (AMIs) as the invariant feature vectors, and the multilayer perceptron (MLP) neural network as the pattern classifier, in developing a road sign recognition system. Six classes of road signs of different position, size and orientation, which were extracted from various near infra-red (NIR) road scenes, had been processed to validate the system. The first four simple AMIs, I1 - I4, were used for the image registration. The AMIs, which were computed from central moments, formed a feature vector that was invariant under the general affine transformation. These feature vectors were then fed into an MLP neural network for classification. The MLP used was trained with the quickprop algorithm (QA), a variation of the standard back-propagation (BPA) algorithm. Scaling and transformation of the feature vectors has reduced its dynamic range significantly towards improving the network convergence and performance. The trained and tested MLP was then validated with a set of feature vectors that it has never “seen” before. This early work has achieved a 100% successful classification rate using a limited validation set of road sign images.

Item Type:Journal
Additional Information:(Notes: There were typos due to the scanning process in the other copy (and the pdf version of the full paper). Will be corrected soon.)
Keywords:Road sign recognition, affine moment invariants (AMIs), multilayer perceptron (MLP), quickprop algorithm (QA)
Subjects:Q Science, Computer Science
ID Code:9060

Annema., A. J. (1995). Feed-Forward Neural Network: Vector Decomposition Analysis, Modelling and Analog Implementation. Kluwer Academic Press.

Bose, N.K. and Liang, P. (1996). Neural Network Fundamentals with Graphs, Algorithms, and Applications. McGraw-Hill Inc.

Budrikis, Z. L. and Hatamian, M. (1984). Moment calculations by digital filters. AT&T Bell Labs. Technical Journal, 63(2):217-229.

Duda, R. O., Hart, P. E. and Stork, D. G. (2001). Pattern Classification. 2nd Ed. John Wiley & Sons.

de la Escalera, A., Armingol, J.M. and Mata, M. (2003). Traffic sign recognition and analysis for intelligent vehicles. Image and Vision Computing, 21:247-258.

Fahlman, S. E. (1989). Faster-learning variations on backpropagation: an empirical study. In G. Hinton, D. Touretzky, and T. Sejnowski (editors). Proc. 1988 Connectionist Models Summer School, Pittsburgh, 38-51. Morgan Kaufmann.

Fang, C. Y., Fuh, C. S., Yen, P. S., Cherng , S. and Chen, S. W. (2004). An Automatic Road Sign Recognition System Based on a Computational Model of Human Recognition Processing. Computer Vision and Image Understanding, 96(2):237-268.

Fang, C.-Y., Chen, S.-W. and Fuh, C.-S. (2003). Road Sign Detection and Tracking. IEEE Transactions on Vehicular Technology, 52(5):1329-1341.

Flusser, J. and Suk, T. (1993). Pattern recognition by affine moment invariants. Pattern Recognition, 26(1):167-174.

Flusser, J. and Suk, T. (1994). Affine moment invariants: a new tool for character recognition.Pattern Recognition Letters, 15:433-436.

Fry, R. C., Rietman, E. A. and Wong C. C. (1991). Back-Propagation Learning and Nonidealities in Analog Neural Network Hardware. IEEE Trans. Neural Networks, 2(1):110-117.

Gibson, C. (editor) (1988). The Facts on File Dictionary of Mathematics, Rev. and expanded ed., Facts on File Inc.

Gonzalez, R. C. and Woods, R. E. (2002). Digital Image Processing. 2nd Ed. Prentice-Hall.

Heikkilä, J. (2004). Pattern matching with affine moment descriptors. Pattern Recognition, 37(9):1825-1834.

Liong, C. Y. (1996). Pattern Recognition using Affine Moment Invariants and Neural Network for Automotive Application. MSc Thesis, Applied Mathematics and Computing, School of Mechanical Engineering, Cranfield University.

Lisboa, P. J. G. (1992). Introduction. In P. G. J. Lisboa (editor). Neural networks: current applications, pp. 1-34. Chapman & Hall.

Magoulas G. D., Plagianakos V. P. and Vrahatis M. N.. (2004). Neural network-based colonoscopic diagnosis using on-line learning and differential evolution. Applied Soft Computing, 4(4):369-379.

Masters, T. (1993). Practical Neural Network Recipes in C++. Academic Press.

Rio, A. (1995). Infra-red Image Enhancement. MSc Thesis, Applied Mathematics and Computing, Schoold of

Mechanical Engineering, Cranfield University.

Rumelhart, D.E., Hinton, G. E. and Williams, R. J. (1986). Learning internal representation by error propagation. In D.E. Rumelhart and J.L. McClelland (editors): Parallel Distributed Processing:Explorations in the Microstructure of Cognition, vol.1, pp. 318-362. MIT Press.

Sonka, M., Hlavac, V. and Roger, B. (1999). Image processing, analysis, and machine vision. 2nd Ed. Brooks/Cole.

Teh, C.H. and Chin, R.T. (1988). On image analysis by method of moments. IEEE Trans. On Pattern Analysis and Machine Intelligence, 10(4):496-512.

(Notes: There were typos due to the scanning process in the other copy. Will be corrected soon.)

Repository Staff Only: item control page
 
 
 
 
 
 
© Copyright 2007 MyAIS Faculty of Computer Science and Information Technology, University of Malaya – All Rights Reserved. Malaysian Abstracting and Indexing System is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.