Abstract
Applying BP to the optimal colors clustering of image can achieve good image segmentation effect. As BP is a teacher-supervised training neural networks algorithm, the correlation of input patterns will result in an insufficient training of some neurons, which will make it fail to segment image. To avoid this, an aimed intensive training method is introduced in this paper, which can achieve satisfactory image segment result.
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References
Yuan, C.: Artificial Neural Network and Its Application, 1st edn. Tsinghua University Press, Beijing (1999)
Bulanon, D.M., Kataoka, T., Ota, Y., et al.: A segmentation algorithm for the automatic recognition of Fuji Apples atharvest. Biosystems Engineering 83(4), 405–412 (2002)
Zhang, Y.: Image Process and Image Analyse. Tsinghua University Press, Beijing (1999)
Ge, S., Jiang, W., Guo, Y.: Color image segmentation based on fuzzy technology and BP neural network. Computer Education 20(4), 126–129 (2008)
Shu, Y.: Typical Cases of Visual C++ Digital Image Recognition Technology, 1st edn. Posts and Telecom Press, Beijing (2008)
Chuandong, W., Lei, C.: A Method for Image Recognition Associative Memory Based on Sparse Exponential Neural Network. Journal of Nanjing University of Posts and Telecommunications 28(6), 78–82 (2008)
Kai, W., Jufeng, Y., Li, W., Guangshun, S., Qingren, W.: Review on generalization problem of artificial neural network. Application Research of Computers 25(12), 3525–3530 (2008)
Yumei, M., Yuhou, W.: Influence of Momentum Gene on BP Algorithms. Journal of The Central University for Nationalities (Natural Sciences Edition) 17(4), 35–40 (2008)
Xiaohua, S., Yanbin, L., Jinshan, H., Da, N.: An improved counter propagation networks and its application. Journal of Central South University: Science and Technology 39(5), 1059–1063 (2008)
Huichun, C.: An BP Neural Network Algorithm for Random Learning Rate. Computer and Digital Engineering 36(10), 25–26 (2008)
Yan, P., Guodong, Z.: Handwritten numerical recognition method based on BP neural network. Journal of Shenyang Institute of Aeronautical Engineering 25(2), 66–69 (2008)
Yuhong, Z., Junwei, W.: Application of Artificial Neural Network for te pH process Identification in Single Loop Neutralizer System. Journal of Hangzhou Dianzi University 28(3), 76–80 (2008)
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Wang, Xr., Yang, Zm., You, My. (2009). An Image Segmentation Method Based on the BP Neural Network. In: Cao, B., Li, TF., Zhang, CY. (eds) Fuzzy Information and Engineering Volume 2. Advances in Intelligent and Soft Computing, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03664-4_38
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DOI: https://doi.org/10.1007/978-3-642-03664-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03663-7
Online ISBN: 978-3-642-03664-4
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