Advertisement

Statistical Multisensor Image Segmentation in Complex Wavelet Domains

  • Tao Wan
  • Zengchang Qin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6979)

Abstract

We propose an automated image segmentation algorithm for segmenting multisensor images, in which the texture features are extracted based on the wavelet transform and modeled by generalized Gaussian distribution (GGD). First, the image is roughly segmented into textured and non-textured regions in the dual-tree complex wavelet transform (DT-CWT) domain. A multiscale segmentation is then applied to the resulting regions according to the local texture characteristics. Finally, a novel statistical region merging algorithm is introduced by measuring a Kullback-Leibler distance (KLD) between estimated GGD models for the neighboring segments. Experiments demonstrate that our algorithm achieves superior segmentation results.

Keywords

multisensor image segmentation statistical modeling complex wavelets 

References

  1. Deng, Y., Manjunath, B.: Unsupervised Segmentation of Color-Texture Regions in Image and Video. IEEE Tran. Pattern Anal. Machine Intell. 23, 800–810 (2001)CrossRefGoogle Scholar
  2. Do, M., Vetterli, M.: Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance. IEEE Tran. Image Process. 11, 146–158 (2002)MathSciNetCrossRefGoogle Scholar
  3. Kingsbury, N.: Complex Wavelets for Shift Invariant Analysis and Filtering of Signals. Appl. Compt. Harmon. Anal. 10, 234–253 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  4. Lewis, J., Nikolov, S., Canagarajah, N., Bull, D., Toet, A.: Uni-Modal Versus Joint Segmentation for Region-Based Image Fusion. In: Proc. of the Int. Conf. Information Fusion, pp. 1–8 (2006)Google Scholar
  5. Lewis, J., O’Callaghan, R., Nikolov, S., Bull, D., Canagarajah, N.: Pixel- and Region-Based Image Fusion with Complex Wavelets. Information Fusion 8, 119–130 (2007)CrossRefGoogle Scholar
  6. Nixon, M., Aguado, A.: Feature Extraction and Image Processing. Academic Press, Oxford (2008)Google Scholar
  7. O’Callaghan, R., Bull, D.: Combined Morphological-Spectral Unsupervised Image Segmentation. IEEE Tran. Image process. 14, 49–62 (2005)CrossRefGoogle Scholar
  8. Simoncelli, E., Anderson, E.: Noise Removal via Bayesian Wavelet Coring. In: Proc. of the IEEE Int. Conf. Image Process., pp. 378–382 (1996)Google Scholar
  9. Wan, T., Canagarajah, N., Achim, A.: Multiscale Color-Texture Image Segmentation with Adaptive Region Merging. In: Proc. of the IEEE Int. Conf. Acoustics, Speech, and Signal Process., pp. 1213–1216 (2007)Google Scholar
  10. Wan, T., Canagarajah, N., Achim, A.: Statistical Multiscale Image Segmentation via Alpha-Stable Modeling. In: Proc. of the IEEE Int. Conf. Image Process., pp. 357–360 (2007)Google Scholar
  11. Wan, T., Canagarajah, N., Achim, A.: Segmentation-Driven Image Fusion Based on Alpha-Stable Modeling of Wavelet Coefficients. IEEE Tran. Multimedia 11, 624–633 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tao Wan
    • 1
  • Zengchang Qin
    • 1
    • 2
  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Intelligent Computing and Machine Learning Lab, School of ASEEBeihang UniversityBeijingChina

Personalised recommendations