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A Novel Pixon-Based Approach for Image Segmentation Using Wavelet Thresholding Method

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Image Analysis and Recognition (ICIAR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5627))

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Abstract

In this paper a novel pixon-based method is proposed for image segmentation, which uses the combination of wavelet transform (WT) and the pixon concept. In our method, a wavelet thresholding technique is successfully used to smooth the image and prepare it to form the pixons. Utilizing the wavelet thresholding leads to elimination of some unnecessary details and results in a fewer pixon number, faster performance and more robustness against unwanted environmental noises. As the next step, the appropriate pixons are extracted and eventually we segment the image with the use of a hierarchical clustering method. The results of applying the proposed method on several different images indicate its better performance in image segmentation compared to the other methods.

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References

  1. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (2004)

    Google Scholar 

  2. Bonnet, N., Cutrona, J., Herbin, M.: A ‘no-threshold’ histogram-based image segmentation method. Pattern Recognition 35(10), 2319–2322 (2002)

    Article  MATH  Google Scholar 

  3. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  4. Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and byes/mdl for multi-band image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18(9), 884–900 (1996)

    Article  Google Scholar 

  5. Papamichail, G.P., Papamichail, D.P.: The k-means range algorithm for personalized data clustering in e-commerce. European Journal of Operational Research 177(3), 1400–1408 (2007)

    Article  MATH  Google Scholar 

  6. Carvalho, F.: Fuzzy c-means clustering methods for symbolic interval data. Pattern Recognition Letters 28(4), 423–437 (2007)

    Article  Google Scholar 

  7. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 1–18 (2002)

    Article  Google Scholar 

  8. Lakshmanan, S., Derin, H.: Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing. IEEE Trans. Pattern Anal. Machine Intell. 11(8), 799–813 (1989)

    Article  Google Scholar 

  9. Kato, Z., Zerubia, J., Berthod, M.: Unsupervised parallel image classification using Markovian models. Pattern Recognit. 32, 591–604 (1999)

    Article  Google Scholar 

  10. Elfadel, I.M., Picard, R.W.: Gibbs random fields, cooccurrences, and texture modeling. IEEE Trans. Pattern Anal. Machine Intell. 16, 24–37 (1994)

    Article  Google Scholar 

  11. Piña, R.K., Pueter, R.C.: Bayesian image reconstruction: The pixon and optimal image modeling. P. A. S. P. 105, 630–637 (1993)

    Article  Google Scholar 

  12. Puetter, R.C.: Pixon-based multiresolution image reconstruction and the quantification of picture information content. Int. J. Imag. Syst. Technol. 6, 314–331 (1995)

    Article  Google Scholar 

  13. Yang, F., Jiang, T.: Pixon-based image segmentation with Markov random fields. IEEE Trans. Image Process. 12(12), 1552–1559 (2003)

    Article  Google Scholar 

  14. Lin, L., Zhu, Yang, L.F., Jiang, T.: A novel pixon-representation for image segmentation based on Markov random field. Image and Vision Computing Journal of Elsevier 26, 1507–1514 (2008)

    Article  Google Scholar 

  15. Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation via wavelet shrinkage. Biometrica 81, 425–455 (1994)

    Article  MATH  Google Scholar 

  16. Jansen, M.: Noise Reduction by Wavelet Thresholding. Springer, New York (2001)

    Book  MATH  Google Scholar 

  17. Chang, S.G., Yu, B., Vetterli, M.: Adaptive Wavelet Thresholding for image Denoising and compression. IEEE Trans. Image Processing 9(9), 1532–1545 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  18. Gupta, S.: kaur, L.: Wavelet Based Image Compression using Daubechies Filters. In: Proc. 8th National conference on communications, I.I.T. Bombay (2002)

    Google Scholar 

  19. Burrus, C.S., Gopinath, R.A., Guo, H.: Introduction to Wavelets and Wavelet Transforms. Prentice Hall, New Jersey (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of information technology and computer engineering, Shahrood University of Technology, Shahrood, Iran

    Hamid Hassanpour

  2. Department of electrical engineering, Iran University of Science and Technology (IUST), Tehran, Iran

    Gholam Ali Rezai Rad & Hadi Yousefian

  3. Department of computer and electrical engineering, Noshirvani University of Technology, Babol, Iran

    Amin Zehtabian

Authors
  1. Hamid Hassanpour
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  2. Gholam Ali Rezai Rad
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  3. Hadi Yousefian
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  4. Amin Zehtabian
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2009 Springer-Verlag Berlin Heidelberg

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Hassanpour, H., Rezai Rad, G.A., Yousefian, H., Zehtabian, A. (2009). A Novel Pixon-Based Approach for Image Segmentation Using Wavelet Thresholding Method. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_19

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  • DOI: https://doi.org/10.1007/978-3-642-02611-9_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02610-2

  • Online ISBN: 978-3-642-02611-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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