Browse > Article
http://dx.doi.org/10.5370/KIEE.2016.65.9.1531

RBFNNs-based Recognition System of Vehicle License Plate Using Distortion Correction and Local Binarization  

Kim, Sun-Hwan (Dept. of Electrical Engineering, The University of Suwon)
Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.65, no.9, 2016 , pp. 1531-1540 More about this Journal
Abstract
In this paper, we propose vehicle license plate recognition system based on Radial Basis Function Neural Networks (RBFNNs) with the use of local binarization functions and canny edge algorithm. In order to detect the area of license plate and also recognize license plate numbers, binary images are generated by using local binarization methods, which consider local brightness, and canny edge detection. The generated binary images provide information related to the size and the position of license plate. Additionally, image warping is used to compensate the distortion of images obtained from the side. After extracting license plate numbers, the dimensionality of number images is reduced through Principal Component Analysis (PCA) and is used as input variables to RBFNNs. Particle Swarm Optimization (PSO) algorithm is used to optimize a number of essential parameters needed to improve the accuracy of RBFNNs. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. Image data sets are obtained by changing the distance between stationary vehicle and camera and then used to evaluate the performance of the proposed system.
Keywords
License plate recognition; Canny algorithm; Local binarization; Image warping; RBFNNs; PCA; PSO;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 [Enforcement 2013.3.1.] [Notice on Ministry of Land, Transport and Maritime Affairs No. 2013-30, Jan. 21th, 2013, Partial amendment] A 5th notice about standard fo vehicle license plate, etc.(A symbol of car's species and use)
2 John Canny, "A computational approach to edge detection," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, pp. 679-698, 1986.   DOI
3 W. Niblack, "An Introduction to Digital Image Processing", Englewood Cliffs, N.J : Prentice Hall, pp. 115-116, 1986.
4 J-H Kim, D-S Noh, "Vehicle License Plate Recognition System By Edge-based Segment Image Generation", The Korea Contents Association, Vol. 12, Issue 3, pp. 9-16, 2012
5 Jin-Seong Jeong, Young-Min Jang, Rrdenetuya, Chang-Hee Park, Hyun-Tae Kim, Sang-Bock Cho, "Vehicle License Plate Recognition System using a Noise-cancelling based on Square Feature and Open CV", The Institute of Electronics Engineers of Korea, pp. 784-786, Jun. 2015
6 F. Shafait, D. Keysers, and T. M. Breuel, "Efficient Implementation of Local Adaptive Thresholding Techniques using Integral Images," SPIE DRR'08, San Jose, CA, USA. 2008(1)
7 W. K. Kim, S. K. Oh, H. K. Kim, "A Study on Feature Selection In Face Image Using Principal Component Analysis and Particle Swarm Optimization Algorithm", KIEE, Vol. 58, No. 12, pp. 2511-2519, 2009
8 S. K. Oh, W. D. Kim, and W. Pedrycz, "Polynomial based radial basis function neural networks(P-RBF NNs) realized with the aid of particle swarm optimization," Fuzzy Sets and Systems, Vol. 163, No. 1, pp. 54-77, 2011   DOI
9 Sung-Kwun Oh, Sung-Hoon Yoo, and Witold Pedrycz, "Design of Object Recognition Algorithm Using PCA-LDA Combined for Hybrid Data Pre-Processing and Polynomial-based RBF Neural Networks : Design and Its Application" Expert Systems with Applications, Vol. 40, Issue 5, pp. 1451-1466, April, 2013   DOI
10 A. Patrikar, J. Porovence, "Pattern classification using polynomial networks," Electronics Letters, Vol. 28, No. 12, pp. 1109-1110, 1992   DOI
11 Sung-Kwun Oh, Wook-Dong Kim, Ho-Sung Park, Myung-Hee Son, "Identification Methodology of FCMbased Fuzzy Model Using Particle Swarm Optimization", The Transactions of The Korean Institute of Electrical Engineers(KIEE), Vol. 60, Issue 1, pp. 184-192, 2011   DOI
12 Won-Ju Hong, Min-Woo Kim, Il-Seok Oh, "Learning-based Detection of License Plate using SIFT and Neural Network", The Institute of Electronics Engineers of Korea, IEIE 50(8), pp. 187-195, 8, 2013
13 M. Y. Kim, Y. D. Kim, "An Approach to Korean License Plate Recognition Based on Vertical Edge Matching", Systems, Man, and Cybernetics, IEEE International Conference, Vol. 4, pp. 2975-2980, Oct. 2000