International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 4 | Issue 8 | Year 2013 | Article Id. IJCTT-V4I8P185 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I8P185

Improved VEDA and Unwanted Edge Removal approaches for License Plate Detection

A.Sravya,P.Ramesh Babu

Citation :

A.Sravya,P.Ramesh Babu, "Improved VEDA and Unwanted Edge Removal approaches for License Plate Detection," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 8, pp. 2858-2862, 2013. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V4I8P185

Abstract

Edge detection is probably the most widely used operations in image analysis, and there are probably more algorithms within the literature for enhancing and detecting edges than any other detection approaches. This is due to the fact that edges establish the outline relevant to an object. An edge will be the boundary between a desire and to discover the background, and indicates the boundary between overlapping objects. Recently, license plate edge detection technique is a vital part of vision navigation, which is the key method of intelligent vehicle assistance. The detection outcome is seriously affected through quality of noise and image. This means that in the event the edges in an image can be identified accurately, all of the objects can easily be located and basic properties such as area, perimeter, and shape can easily be measured. License plate edge detection is assist to analyze the direction of the plate extension and the specific location of obstacles, dimension and speed of obstacles among the road. Within the existing work, several typical edge detection operators like Prewitt, Sobel operators and in digital image processing are theoretically assessed, and are generally made use of for license edge detection. By evaluating the existing simulation results of license edge detection, the higher quality road test results might be gained when using Sobel and Prewitt operator[1].Existing work doesn’t support if the input image is noisy image. In our proposed work we implemented Adaptive median filter for edge detection algorithms in order to eliminate the noisy. In our proposed work we also introduce improved edge detection ULEA and VEDA methods which gives the higher quality result in the presence of noise. The effectiveness of the improved process is demonstrated experimentally. The current work mainly concentrates on the study of different edge detection techniques and analysis of there relative performances.

Keywords

Edge Detection, License Plate, VEDA, Filtering ,Vertical lines,Pixels.

References

[1]  Study on the Edge Detection Algorithms of Road   Image, Chun-ling FanYuan-yuan Ren, ISIP ‘10 Proceedings of the 2010 Third International Symposium on Information Processing Pages 217220  IEEE Computer Society Washington, DC, USA ©2010.
[2] License Plate Localization from Vehicle Images: An Edge Based Multi-stage Approach, Satadal Saha1, Subhadip Basu, International Journal of Recent Trends in Engineering, Vol 1, No. 1, May 2009
[3] Color Edge Detection Algorithm in RGB Color Space Soumya Dutta, Bidyut B. Chaudhuri, 2009 International Conference on Advances in Recent Technologies in Communication and Computing. 
[4]  A New Vertical Edge Detection Algorithm and its Application Abbas M. Al-Ghaili, Syamsiah Mashohor, Alyani Ismail, and Abdul Rahman Ramli,ieee conference 2008 .
[5] http://www.yildiz.edu.tr/~bayram/sgi/saygi.htm .
[6] An Algorithm for License Plate Recognition Using Radial Basis Function Neural  Network Bo Li; Zhiyuan Zeng; Jian-zhong Zhou; Hua-li Dong; Computer Science  and Computational Technology, 2008. ISCSCT '08. International Symposium on 44 Volume 1, 20-22 Dec. 2008 Page(s):569 - 572 Digital Object Identifier  10.1109/ISCSCT.2008.272
[7] Distance and Color Invariant Automatic License Plate Recognition System Khan,  N.Y.; Imran, A.S.; Ali, N.; Emerging Technologies, 2007. ICET 2007. International  Conference on 12-13 Nov. 2007 Page(s):232 - 237 Digital Object Identifier  10.1109/ICET.2007.4516349.
[8] Distance and Color Invariant Automatic License Plate Recognition System Khan,  N.Y.; Imran, A.S.; Ali, N.; Emerging Technologies, 2007. ICET 2007. International  Conference on 12-13 Nov. 2007 Page(s):232 - 237 Digital Object Identifier  10.1109/ICET.2007.4516349.
[9] Graphical Models for Joint Segmentation and Recognition of License Plate  Characters Xin Fan; Guoliang Fan; Signal Processing Letters, IEEEVolume 16,  Issue 1, Jan. 2009 Page(s):10 – 13 Digital Object 10.1109/LSP.2008.2008486. Identifier 
[10] Character recognition using parallel BP neural network Feng Yang; Fan Yang;  Audio, Language and Image Processing, 2008. ICALIP 2008. International  Conference on 7-9 July 2008 Page(s):1595 - 1599 Digital Object Identifier  10.1109/ICALIP.2008.4590169 
[11] Chinese License Plate Recognition Using a Convolutional Neural Network  Zhihong Zhao; Shaopu Yang; Xinna Ma; Computational Intelligence and Industrial  Application, 2008. PACIIA '08. Pacific-Asia Workshop on Volume 1, 19-20 Dec.  2008 Page(s):27 - 30 Digital Object Identifier 10.1109/PACIIA.2008.196 
[12] Color edge enhancement based fuzzy segmentation of license plates Syed, Y.A.;  Sarfraz, M.; Information Visualisation, 2005. Proceedings. Ninth International  Conference on 6-8 July 2005 Page(s):227 - 232 Digital Object Identifier  10.1109/IV.2005.33