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Road Sign Segmentation and Recognition under Bad Illumination Condition using Modified Fuzzy C-means Clustering

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 8
Year of Publication: 2012
Zinat Afrose
Md. Al-amin Bhuiyan

Zinat Afrose and Md. Al-amin Bhuiyan and. Article: Road Sign Segmentation and Recognition under Bad Illumination Condition using Modified Fuzzy C-means Clustering. International Journal of Computer Applications 50(8):1-6, July 2012. Full text available. BibTeX

	author = {Zinat Afrose and Md. Al-amin Bhuiyan and},
	title = {Article: Road Sign Segmentation and Recognition under Bad Illumination Condition using Modified Fuzzy C-means Clustering},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {8},
	pages = {1-6},
	month = {July},
	note = {Full text available}


In this paper, we present a novel approach on road sign segmentation under bad lighting condition employing a modified fuzzy c-means clustering. Most of the cases accidents occur in bad weather or when the road signs cannot be recognized. The proposed system implements a system that segments the road sign using fuzzy c-means clustering. At first, the image is detected in RGB colour space and then converted into HSV colour model. After the conversion we detected the edge by applying Canny edge detector. Thus the proposed method is applied on the image. The method is based on the local similarity measure so that the noise created or the blurred image can easily be detected. But the traditional Fuzzy C-means clustering lack enough robustness to noise and preserving details of the image. Experimental results demonstrate that the system can segment the road signs successfully under bad illumination conditions. This system can be applied to typical type of traffic signs such as triangular, circle etc. The output result of the system is encouraging as the accuracy rate is 99%.


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