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An Improvement on Competitive Neural Networks Applied to Image Segmentation

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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Abstract

Image segmentation is a long existing problem and still regarded as unsolved to a large extent in computer vision. This letter describes the modeling method of competitive neural networks and elucidates its connection with the Hopfield type optimization network. A new algorithm to map the image segmentation problem onto competitive networks is proposed and its convergence is shown by the stability analysis. Finally, the improvement on the competitive neural networks based method is validated by the simulation results.

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References

  1. Pal, N.R., Pal, S.K.: A Review on Image Segmentation Techniques. Pattern Recognition 26, 1277–1294 (1993)

    Article  Google Scholar 

  2. Egmont-Pertersen, M., Ridder, D., Handels, H.: Image Processing with Neural Networks–a Review. Pattern Recognition 35, 2279–2301 (2002)

    Article  Google Scholar 

  3. Wang, D.L., Terman, D.: Image Segmentation based on Oscillatory Correlation. Neural Computation 9, 805–836 (1997)

    Article  Google Scholar 

  4. Pavlidis, T., Liow, Y.: Integrating Region Growing and Edge Detection. IEEE Trans. Pattern Analysis and Machine Intelligence 12, 225–233 (1990)

    Article  Google Scholar 

  5. Gonzalez, R., Woods, R.: Digital Image Processing, 2nd edn. Prentice Hall, New Jersey (2002)

    Google Scholar 

  6. Cheng, K., Lin, J., Mao, C.: The Application of Competitive Hopfield Neural Network to Medical Image Segmentation. IEEE Trans. on Medical Imaging 15, 560–567 (1996)

    Article  Google Scholar 

  7. Wu, Y., Yang, C., Wang, T.: A New Approach of Color Quantization of Image based on Neural Network. In: International Joint Conference on Neural Networks, pp. 973–977 (2001)

    Google Scholar 

  8. Gopal, S., Sahiner, B., Chan, H., Petrick, N.: Neural Network based Segmentation Using a Priori Image Models. In: International Conference on Neural Networks, vol. 4, pp. 2455–2459 (1997)

    Google Scholar 

  9. Phoha, V., Oldham, W.: Image Recovery and Segmentation Using Competitive Learning in a Layered Network. IEEE Trans. Neural Networks 7, 843–856 (1996)

    Article  Google Scholar 

  10. Hopfield, J., Tank, D.: Neural Computation of Decision in Optimization Problem. Biol. Cybern. 52, 141–152 (1985)

    MATH  MathSciNet  Google Scholar 

  11. Peng, M., Narendra, K., Gupta, A.: An Investigation into the Improvement of Local Minimum of the Hopfield Network. Neural Networks 9, 1241–1253 (1996)

    Article  Google Scholar 

  12. Tang, H., Tan, K.C., Yi, Z.: A Columnar Competitive Model for Solving Combinatorial Optimization Problems. IEEE Trans. Neural Networks 15, 1568–1573 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligent Systems Center, Nanyang Technical University, 50, Nanyang Drive, 7th Storey, Research Techno Plaza, Border X Block, 637553, Singapore

    Rui Yan & Meng Joo Er

  2. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Huajin Tang

Authors
  1. Rui Yan
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  2. Meng Joo Er
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  3. Huajin Tang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Yan, R., Er, M.J., Tang, H. (2006). An Improvement on Competitive Neural Networks Applied to Image Segmentation. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_73

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  • DOI: https://doi.org/10.1007/11760023_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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