Edge Detection of the Impulse Noise Pollution Image Based on the Rough Set

Abstract:

The rough set is an effective tool to deal with incomplete, uncertain and fuzzy problem. In order to extract the edges information of the image by impulse noise pollution, further service for the follow-up image analysis and understanding, on the basis of the impulse noise pollution image preprocessing, an adaptive edge detection algorithm is put forward to process the impulse pollution image using rough set theory in this paper. Considering the target edge features can form a series of edge point constraints as the algorithm foundation,this method starts from the characteristics difference of the image edge and target area. Firstly, series edge constraint points are defined, and then the related questions of these conditions are solved by using rough set theory, so the edge detection algorithm is established. The experimental results show that this algorithm can effectively extract edge from the noise image information, to overcome the sensitivity of traditional algorithm for noise.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 904-907
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.904
• Online since: July 2013
• Authors: Qing Liu*, Hong Bo Wei, Xiao Ping Yang
• Keywords: Edge Detection, Image with Impulse Noise, Rough Set (RS)

References

[1] Yue Gaili, Wang Dong. Application of several edge detection operator in infrared images processing. Journal of Xi'an University of Science and Technology, Vol. 32(2012), pp.500-504.

[2] Tian Yuanyuan, Kong Yincheng, Cui Kai, et al. Precise of Edge Orientation Affected by Noise. Journal of Wuhan University of Technology, Vol. 34(2012), pp.141-145.

[3] Dorin Comaniciu. Mean-shift: A Robust Approach toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence，Vol. 24(2002), pp.603-619.

DOI: 10.1109/34.1000236

[4] Song X, Luo J, Wang L P. Edge Detection Method Based on Mean Shift Clustering Method. Journal of Projectiles，Rockets，Missiles and Guidance，Vol. 27(2007), pp.366-368.

[5] Yu Qianzhao, Wei Huagui. Edge Detection Based on Multi-structure Elements Morphology. Proc. of the 6th World Congress on Intelligent Control and Automation, 2006, pp.9795-9798.

DOI: 10.1109/wcica.2006.1713908

[6] Zheng Ling, Zhang Bin, Lin Jie, et al. Multi-scale Edge Detection in the EMD Domain Using the Adaptive Noise Threshold. Computer Science, Vol. 39(2012), pp.552-554.

DOI: 10.1109/iccsnt.2011.6182050

[7] BaiJianjun, Zhang Xin, Chen Qisong. Edge Detection Algorithm Based on Self-adaptive Filter for Noise Image. Computer Simulation, Vol. 3(2012), pp.334-337.

[8] Z. Pawlak. Rough Sets．International Journal of Computer and Information Science，Vol. 11(1982), pp.241-356.