An Image Positioning Method of Automatic Random Walker Based on IFS Edge Detection

Abstract:

The segmentation method of classical random walker needs to select seed points artificially, and may cause incomplete or inaccurate segmentation. In view of these problems, this paper presents an image positioning method of automatic random walker based on IFS (Intuitionistic Fuzzy Set). IFS edge detection method is used to get the edge information of image, and then the connected domains are found out in image edge using the morphology method. Select the central pixels of each connected domain as seed points for automatic random walker, and then segment the IFS edge image by random walker. Experimental results show that the proposed method overcomes the application limitation of semi-automatic random walker algorithm, and improves the accuracy of image positioning.

• Periodical: Applied Mechanics and Materials (Volumes 599-601)
• Pages: 802-806
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.599-601.802
• Online since: August 2014
• Authors: Yin Cheng Qi*, Ting Li, Ming Xiao Xi, Zhen Bing Zhao, Yin Ping Cai
• Keywords: Edge Detection, IFS, Image Positioning, Random Walker

References

[1] Z. Kato, T.C. Pong, S.G. Qiang: Multicue MRF Image Segmentation: Combining Texture and Color Features. 16th International Conference on Pattern Recognition, Vol. 1 (2002), pp.660-663.

DOI: 10.1109/icpr.2002.1044836

[2] X. Wei, W. Shen: Image Segmentation Based on Gabor Filter and MRF Model. 5th International Conference on Wireless Communications, Networking and Mobile Computing (2009), pp.1-4.

DOI: 10.1109/wicom.2009.5301963

[3] L. Zhao, C.H. Li, F.N. Dang, D.F. Chen, S.J. Xu: Concrete CT Image Segmentation Method Based on Markov Random Field. 29th Chinese Control Conference CCC (2010), pp.2677-2680.

[4] M. Lenic, D. Zazula, B. Cigale: Segmentation of Ovarian Ultrasound Images Using Single Template Cellular Neural Networks Trained with Support Vector Machines. 20th IEEE International Symposium on Computer-Based Medical Systems (2007), pp.205-212.

DOI: 10.1109/cbms.2007.97

[5] S.H. Luo, Q.M. Hu, X.J. He, J.M. Li, J.S. Jin, M. Park: Automatic Liver Parenchyma Segmentation from Abdominal CT Images Using Support Vector Machines. ICME International Conference on Complex Medical Engineering (2009), pp.1-5.

DOI: 10.1109/iccme.2009.4906625

[6] F. Du, W.K. Shi., Y. Deng: Infrared Image Sequence Segmentation Based on Support Vector Machine. Opto-Electronic Engineering, Vol. 32, No. 3 (2005), pp.62-65, in Chinese.

[7] A. Oliver, X. Munoz, J. Batlle, L. Pacheco, J. Freixenet: Improving Clustering Algorithms for Image Segmentation Using Contour and Region Information. IEEE International Conference on Automation, Quality and Testing, Robotics (2006), pp.315-320.

DOI: 10.1109/aqtr.2006.254652

[8] S.N. Sulaiman, N.A.M. Isa: Adaptive Fuzzy-K-means Clustering Algorithm for Image Segmentation. IEEE Transactions on Consumer Electronics, Vol. 56, No. 4 (2010), pp.2661-2668.

DOI: 10.1109/tce.2010.5681154

[9] L. Grady: Random Walks for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 11 (2006), pp.1768-1783.

DOI: 10.1109/tpami.2006.233

[10] L. Grady: Multilabel Random Walker Image Segmentation Using Prior Models. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1 (2005), pp.763-770.

DOI: 10.1109/cvpr.2005.239

[11] Y.F. Yi, L.Q. Gao, L. Guo: Application of Improved FCM for Interactive Image Segmentation. Journal of Image and Graphics, Vol. 17, No. 3 (2012), pp.342-348, in Chinese.

[12] H.C. Zhao, W.N. Li, G. Pan, L.Q. Gao: An Improved Random Walker Using Spatial Feature for Image Segmentation. 25th Chinese Control and Decision Conference (2013), pp.1479-1482.

DOI: 10.1109/ccdc.2013.6561160

[13] S.G. Chen, Z.X. Sun, J. Zhou: Semi-supervised Image Segmentation Based on Integration of SSFCM with Random Walks. Journal of Computer-Aided Design & Computer Graphics, Vol. 25, No. 7 (2013), pp.1074-1082, in Chinese.

[14] T. Chaira, A. K. Ray: A New Measure Using Intuitionistic Fuzzy Set Theory and Its Application to Edge Detection. Applied Soft Computing, Vol. 8, No. 2 (2008), pp.919-927.

DOI: 10.1016/j.asoc.2007.07.004