A Novel Method for Grayscale Image Segmentation by Using GIT-PCANN
Автор: Haiyan Li, Guo Lei, Zhang Yufeng, Xinling Shi, Chen Jianhua
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 5 Vol. 3, 2011 года.
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PCNN has been widely used in image segmentation. However, satisfactory results are usually obtained at the expense of time-consuming selection of PCNN parameters and the number of iteration. A novel method, called grayscale iteration threshold pulse coupled neural network (GIT-PCNN) was proposed for image segmentation, which integrates grayscale iteration threshold with PCNN. In this method, traditional PCNN is simplified so that there is only one parameter to be determined. Furthermore, the PCNN threshold is determined iteratively by the grayscale of the original image so that the image is segmented through one time of firing process and no iteration or specific rule is needed as the iteration stop condition. The method demonstrates better performance and faster compared to those PCNN based segmentation algorithms which require the number of iterations and image entropy as iteration stop condition. Experimental results show the effectiveness of the proposed method on segmentation results and speed performance.
Image segmentation, Pulse Coupled Neural Network (PCNN), GIT-PCNN(Grayscale Iteration Threshold PCNN)
Короткий адрес: https://sciup.org/15011638
IDR: 15011638
Список литературы A Novel Method for Grayscale Image Segmentation by Using GIT-PCANN
- H. Zhang, J.E. Fritts, S. A. Goldman, “Image segmentation evaluation: A survey of unsupervised methods”, Computer Vision and Image Understanding, vol. 110(2008), pp.260-280, 2008.
- G. Kuntimad, H.S. Ranganath, “Perfect image segmentation using pulse coupled neural networks”, IEEE Transactions on Neural Networks. Vol.10 (3), pp. 591-598, 1999.
- R D Stewart, I Fermin, and M Opper. “Region growing with pulse –coupled neural networks: an alternative to seeded region growing”. IEEE Trans. on Neural Network, vol. 13(6),pp. 1557-1562, 2002.
- J.A. Karvonen, “Baltic sea ice SAR segmentation and classification using modified pulse-coupled neural networks”, IEEE Transactions on Geoscience and Remote Sensing, vol. 42 (7) ，pp.1566-1574, 2004.
- K.M. Iftekharuddln, M. Prajna, S. Samanth, M. Indhukuril, Mege voltage “X-ray image segmentation and ambient noise remova”, in: Proc. of the 2nd Joint EMBSlBMES Conference, pp. 1111-1113,2002.
- M.I.M. Chacon, S.A. Zimmerman, “License plate location based on a dynamic PCNN scheme”, in: Proc. of the International Joint Conference on Neural Networks, vol. 2, pp. 1195-1200, 2003.
- D. Yamaoka, Y. Ogawa, K. Ishimura, M. Wada, “Motion segmentation using pulse-coupled neural network”, SICE kmual Conference in Fukui, pp. 2778-2783, 2003.
- X.F. Zhang, A. A. Minai, Temporally sequenced intelligent block-matching and motion-segmentation using locally coupled networks [J], IEEE Transactions on Neural Networks 15 (5), pp. 1202-1214, 2004.
- Y. Ma, C. Qi, Region labeling method based on double PCNN and morphology [J], in: Proc. of ISCIT, pp. 321-324, Oct. 2005, Beijing, China.
- M. Guo, L. Wang, X. Yuan, Car plate localization using pulse coupled neural network in complicated environment[J], in: Proc. of the 9th Pacific Rim International Conference on Artificial Intelligence, pp. 1206-1210,Sep. 2006, Guangxi, China.
- X.D.Gu, L.M.Zhang and D.H.Yu. Automatic image segmentation using Unit-linking PCNN without choosing parameters(in Chinese) [J]. Journal of circuits and systems. 12(6), pp.54-59, 2007.
- R.C.Nie, D.M.Zhou and D.F.Zhao，Image Segmentation New Methods Using Unit-Linking PCNN and Image’s Entropy (in Chinese)[J]，Journal of System Simulation， 20(1),pp.222-227 2008.
- Levine M. D, Nazif. A. M. Dynamic measurement of computer generated image segmentations [J]. IEEE Trans on Pattern Analysis and Machine Intelligence , 7 (2) : pp.155-164, 1985.
