Image Segmentation Using Adaptive Potential Active Contours

  • Arkadiusz Tomczyk
Part of the Advances in Soft Computing book series (AINSC, volume 45)


In the paper a new segmentation method is proposed. Adaptive potential active contour approach (APAC) is a result of the relationship between active contour methods and classifier construction techniques. As an optimization algorithm simulated annealing was used to avoid local minima of energy function. The presented approach allows to obtain contours of various topology and a new adaptation mechanism can be introduced to improve segmentation results.


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  1. 1.
    Kass M., Witkin W., Terzopoulos D.: Snakes: Active Contour Models. International Journal of Computer Vision. (1988) 321–331.Google Scholar
  2. 2.
    Cootes T., Taylor C., Cooper D., Graham J., Active Shape Model-Their Training and Application, CVGIP Image Understanding, (1994) 61(1) 38–59.Google Scholar
  3. 3.
    Caselles V., Kimmel R., Sapiro G.: Geodesic Active Contours. International Journal of Computer Vision 22(1) (1997) 61–79.zbMATHCrossRefGoogle Scholar
  4. 4.
    Grzeszczuk R., Levin D., Brownian Strings: Segmenting Images with Stochastically Deformable Models, IEEE Transactions on Pattern Analysis and Machine Intelligence (1997) vol. 19 no. 10 1100–1013.CrossRefGoogle Scholar
  5. 5.
    Tadeusiewicz R., Flasinski M., Pattern Recognition, PWN, Warsaw, (1991) (in Polish).Google Scholar
  6. 6.
    Bishop Ch.: Neural Networks for Pattern Recognition. Clarendon Press. Oxford (1993).Google Scholar
  7. 7.
    Looney C: Pattern Recognition Using Neural Networks. Theory and Algorithms for Engineers and Scientists. Oxford University Press. New York (1997).Google Scholar
  8. 8.
    Gonzalez R., Woods R., Digital Image Processing, Prentice-Hall Inc., New Jersey (2002).Google Scholar
  9. 9.
    Kirkpatrick S., Gerlatt C. D. Jr., Vecchi M.P.: Optimization by Simulated Annealing. Science 220. (1983) 671–680.CrossRefMathSciNetGoogle Scholar
  10. 10.
    Tomczyk A., Szczepaniak P. S.: On the Relationship between Active Contours and Contextual Classification. Computer Recognition Systems. Proceedings of the 4th International Conference on Computer Recognition Systems, CORES’05. Poland. Springer Berlin, Heidelberg, New York. (2005) 303–311.Google Scholar
  11. 11.
    Tomczyk A.: Active Hypercontours and Contextual Classification. Intelligent Systems Design and Applications Proceedings of 5th International Conference on Intelligent Systems Design and Applications, ISDA’05. IEEE Computer Society Press. (2005) 256–261.Google Scholar
  12. 12.
    Tomczyk A., Szczepaniak P. S.: Adaptive Potential Active Hypercontours. 8th International Conference on Artificial Intelligence and Soft Computing (ICAISC). Springer-Verlag Berlin, Heidelberg. (2006) 692–701.Google Scholar
  13. 13.
    Tomczyk A., Szczepaniak P. S.: Contribution of Active Contour Approach to Image Understanding. Proceedings of the 2007 IEEE International Workshop on Imaging Systems and Techniques (IST). Krakow. Poland, ISBN: 1-4244-0965-9 (2007).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Arkadiusz Tomczyk
    • 1
  1. 1.Institute of Computer ScienceTechnical University of LodzLodzPoland

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