Skip to main content
Springer Nature Link
Log in

Human Understandable Features for Segmentation of Solid Texture

  • Conference paper
Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5875))

Included in the following conference series:

Abstract

The purpose of this paper is to present new texture descriptors dedicated to segmentation of solid textures. The proposed texture attributes are inspired by the human description of texture and allows a general description of texture. Moreover it is more convenient for a user to understand features signification particularly in a man-aided application. In comparison with psychological measurements for human subjects, our characteristics gave good correspondences in rank correlation of 12 different solid textures. Using these texture features, segmentation results obtained with the classical K-means method on solid textures and real three-dimensional ultrasound images of the skin are presented and discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Tuceryan, M., Jain, A.K.: 2.1. In: Texture Analysis. The Handbook of Pattern Recognition and Computer Vision, pp. 207–248 (1998)

    Google Scholar 

  2. Haralick, R.M.: Statistical and structural approaches to textures. Proceedings of the IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  3. Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610–621 (1973)

    Article  Google Scholar 

  4. Tuceryan, M., Jain, A.K.: Texture segmentation using voronoi polygons. IEEE Transactions On Pattern Analysis And Machine Intelligence 12, 211–216 (1990)

    Article  Google Scholar 

  5. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE transaction on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)

    Article  MATH  Google Scholar 

  6. Chellappa, R., Jain, A.K.: Markov Random Fields Theory and Application. Academic Press, London (1993)

    Google Scholar 

  7. Tamura, H., Mori, S., Yamawaki, T.: Texture features corresponding to visual perception. IEEE transaction on Systems, Man, Cybernetics 8(6), 460–473 (1978)

    Article  Google Scholar 

  8. Amadasun, M., King, R.: Texture features corresponding to textural properties. IEEE transactions on Systems, Man and Cybernetics 19(5), 1264–1274 (1989)

    Article  Google Scholar 

  9. Suzuki, M.T., Yoshitomo, Y., Osawa, N., Sugimoto, Y.: Classification of solid textures using 3d mask patterns. In: ICSMC 2004: International Conference on Systems, Man and Cybernetics (2004)

    Google Scholar 

  10. Noble, J.A., Boukerroui, D.: Ultrasound image segmentation: A survey. IEEE Transactions on Medical Imaging 25(8), 987–1010 (2006)

    Article  Google Scholar 

  11. Coleman, G., Andrews, H.: Image segmentation by clustering. Proceedings of the IEEE, 773–785 (1979)

    Google Scholar 

  12. Shoshany, M.: An evolutionary patch pattern approach for texture discrimination. Pattern Recognition 41, 2327–2336 (2008)

    Article  MATH  Google Scholar 

  13. Chassery, J.M., Montanvert, A.: Géométrie discrète en analyse d’images (1991)

    Google Scholar 

  14. Zhang, J., Tan, T.: Brief review of invariant texture analysis methods. Pattern Recognition 35, 735–747 (2002)

    Article  MATH  Google Scholar 

  15. Goyal, R., Goh, W., Mital, D., Chan, K.: Scale and rotation invariant texture analysis based on structural property. In: IECON 1995: Proceedings on the International Conference on Industrial Electronics, Control, and Instrumentation (1995)

    Google Scholar 

  16. Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing 4, 1549–1560 (1995)

    Article  Google Scholar 

  17. Muneeswaran, K., Ganesan, L., Arumugam, S., Soundar, K.R.: Texture classification with combined rotation and scale invariant wavelet features. Pattern Recognition 38, 1495–1506 (2005)

    Article  MATH  Google Scholar 

  18. Kopf, J., Fu, C.-W., Cohen-Or, D., Deussen, O., Lischinski, D., Wong, T.-T.: Solid texture synthesis from 2d exemplars. In: SIGGRAPH 2007: Proceedings of the 34th International Conference and Exhibition on Computer Graphics and Interactive Techniques (2007)

    Google Scholar 

  19. Paulhac, L., Makris, P., Ramel, J.Y.: A solid texture database for segmentation and classification experiments. In: VISSAPP 2009: Proceedings of the Fourth International Conference on Computer Vision Theory and Applications (2009)

    Google Scholar 

  20. Cardoso, J.S., Corte-Real, L.: Toward a generic evaluation of image segmentation. IEEE Transaction on Image Processing 14(11), 1773–1782 (2005)

    Article  Google Scholar 

  21. Gusfield, D.: Partition-distance: A problem and class of perfect graphs arising in clustering. Information Processing Letters 82(9), 159–164 (2002)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire Informatique (EA2101), Université François Rabelais Tours,  

    Ludovic Paulhac, Pascal Makris & Jean-Yves Ramel

  2. UMR INSERM U930, CNRS ERL 3106, équipe 5, Université François Rabelais Tours,  

    Jean-Marc Gregoire

Authors
  1. Ludovic Paulhac
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pascal Makris
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Marc Gregoire
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jean-Yves Ramel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, Saitama, Japan

    Yoshinori Kuno

  6. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, P.O. Box, 119613, Singapore

    Junxian Wang

  7. Department of Computer Science & Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.

    Jun-Xuan Wang

  8. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  9. Department of Informatics, Univ. of Zurich, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  10. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  11. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  12. Humana Inc., 40202, Louisville, KY, USA

    Miguel L. Encarnação

  13. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  14. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Paulhac, L., Makris, P., Gregoire, JM., Ramel, JY. (2009). Human Understandable Features for Segmentation of Solid Texture. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10331-5_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10330-8

  • Online ISBN: 978-3-642-10331-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics