A Multiphase Level Set Evolution Scheme for Aerial Image Segmentation Using Multi-scale Image Geometric Analysis

  • Wang Wei
  • Yang Xin
  • Cao Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


This paper describes a new aerial images segmentation algorithm. The algorithm is based upon the knowledge of image multi-scale geometric analysis which can capture the image’s intrinsic geometrical structure efficiently. The Contourlet transform is selected to represent the maximum information of the image and obtain the rotation invariant features of the image. A modified Mumford-Shah model is built to segment the aerial image by a necessary level set evolution. To avoid possible local minima in the level set evolution, we control the value of weight numbers of features in different evolution periods in this algorithm, instead of using the classical technique which evolve in a multi-scale fashion.


Aerial Image Laplacian Pyramid Redundancy Ratio Rotation Invariant Feature Multiscale Geometric Analysis 


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  1. 1.
    Li, J., Najmi, A., Gray, R.M.: Image Classification by a Two-Dimensional Hidden Markov Model. IEEE Transactions on Signal Processing 48(2), 517–533 (2000)CrossRefGoogle Scholar
  2. 2.
    Reno, A.L., Booth, D.M.: Using models to recognise man-made objects, Visual Surveil-lance. In: Second IEEE Workshop, June 26, pp. 33–40 (1999)Google Scholar
  3. 3.
    Solka, J.L., Marchette, D.J., Wallet, B.C.: Identification of Man-Made Regions in Unmanned Aerial Vehicle Imagery and Videos. IEEE Transactions on PAMI 20(8), 852–857 (1998)Google Scholar
  4. 4.
    Carlotto, M.J.: Detecting Man-Made Features in SAR Imagery. In: International Remote Sensing for a Sustainable Future of Geoscience and Remote Sensing Symposium, IGARSS 1996, vol. 1, pp. 27–31 (May 1996)Google Scholar
  5. 5.
    Lebitt, S., Aghdasi, F.: Texture Measures for Building Recognition in Aerial Photographs. In: Proceedings of the 1997 South African Symposium on Communications and Signal Processing, COMSIG 1997, September 9-10 (1997)Google Scholar
  6. 6.
    Candès, E.J.: Ridgelets: Theory and Applications. Department of Statistics, Stanford University, USA (1998)Google Scholar
  7. 7.
    Do, M.N.: Contourlets: A new directional multiresolution image representation. In: Conference Record of the Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 497–501 (2002)Google Scholar
  8. 8.
    Do, M.N.: Contourlets and Sparse Image Expansions. Proceedings of SPIE - The International Society for Optical Engineering 5207(2), 560–570 (2003)MathSciNetGoogle Scholar
  9. 9.
    Lu, Y., Do, M.N.: Crisp-contourlets: A critically sampled directional multiresolution image representation. In: Proc. SPIE Conf. Wavelet Applications Signal Image Process, San Diego, CA (August 2003)Google Scholar
  10. 10.
    Bamberger, R.H., Smith, M.J.T.: A filterbank for the directional decomposition of images: Theory and design. IEEE Trans. Signal Process. 40(7), 882–893 (1992)CrossRefGoogle Scholar
  11. 11.
    Nguyen, T.T., Oraintara, S.: Multiresolution Direction Filterbanks: Theory, Design, and Applications. IEEE Transactions on Signal Processing 53(10), 3895–3905 (2005)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Kokare, M., Biswas, P.K., Chatterji, B.N.: Rotation Invariant Texture Features Using Rotated Complex Wavelet For Content Based Image Retrieval. In: International Conference on Image Processing (ICIP), pp. 393–396 (2004)Google Scholar
  13. 13.
    Mumford, D., Shah, J.: Optimal approximation by piece wise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 42(5), 577–685 (1989)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Vese, L.A., Chan, T.F.: A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model. International Journal of Computer Vision 50(3), 271–293 (2002)MATHCrossRefGoogle Scholar
  15. 15.
    Aujol, J.-F., Aubert, G., Blanc-Féraud, L.: Wavelet-Based Level Set Evolution for Classification of Textured Images. IEEE Transactions on Image Processing 12(12), 1634–1641 (2003)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Guo, C., Xin, Y., Mao, Z.: A two-stage level set evolution scheme for man-made objects detection in aerial images. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 474–479 (2005)Google Scholar
  17. 17.
    Geiger, D., Gupta, A., Costa, L.A., Vlontzos, J.: Dynamic programming for detecting, tracking, and matching deformable contours. IEEE-PAMI, 17(3) (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wang Wei
    • 1
  • Yang Xin
    • 1
  • Cao Guo
    • 1
  1. 1.Institute of Image Processing and Mode RecognitionShanghai Jiaotong UniversityShanghaiP.R. China

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