Texture Boundary Detection for Real-Time Tracking

  • Ali Shahrokni
  • Tom Drummond
  • Pascal Fua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3022)


We propose an approach to texture boundary detection that only requires a line-search in the direction normal to the edge. It is therefore very fast and can be incorporated into a real-time 3–D pose estimation algorithm that retains the speed of those that rely solely on gradient properties along object contours but does not fail in the presence of highly textured object and clutter.

This is achieved by correctly integrating probabilities over the space of statistical texture models. We will show that this rigorous and formal statistical treatment results in good performance under demanding circumstances


Hide Markov Model Texture Segmentation Cluttered Background Detect Change Point Texture Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Drummond, T., Cipolla, R.: Real-time tracking of highly articulated structures in the presence of noisy measurements. In: International Conference on ComputerVision,Vancouver, Canada (2001)Google Scholar
  2. 2.
    Tomasi, C., Kanade, T.: Shape and Motion from Image Streams under Orthography: A Factorization Method. International Journal of Computer Vision 9, 137–154 (1992)CrossRefGoogle Scholar
  3. 3.
    Fitzgibbon, A., Zisserman, A.: Automatic Camera Recovery for Closed or Open Image Sequences. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 311–326. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Pollefeys, M., Koch, R., Van Gool, L.: Self-Calibration and Metric Reconstructio In Spite of Varying and UnknownInternal Camera Parameters. In: International Conference on Computer Vision (1998)Google Scholar
  5. 5.
    Marchand, E., Bouthemy, P., Chaumette, F., Moreau, V.: Robust real-time Visual Tracking Using a 2D-3D Model-Based Approach. In: International Conference on Computer Vision, Corfu, Greece, pp. 262–268 (1999)Google Scholar
  6. 6.
    Lowe, D.G.: Robust model-based motion tracking through the integration of search and estimation. International Journal of Computer Vision 8(2) (1992)Google Scholar
  7. 7.
    Vacchetti, L., Lepetit, V., Fua, P.: Fusing Online and Offline Information for Stable 3– D Tracking in Real-Time. In: Conference on Computer Vision and Pattern Recognition, Madison, WI (2003)Google Scholar
  8. 8.
    Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recognition 23(12), 1167–1186 (1991)CrossRefGoogle Scholar
  9. 9.
    Bovik, A., Clark, M., Geisler, W.: Multichannel Texture Analysis Using Localized Spatial Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 55–73 (1990)CrossRefGoogle Scholar
  10. 10.
    Puzicha, J., Buhmann, J.M.: Multiscale annealing for grouping and unsupervised texture segmentation. Computer Vision and Image Understanding: CVIU 76, 213–230 (1999)CrossRefGoogle Scholar
  11. 11.
    Bouman, C., Liu, B.: Multiple Resolution Segmentation of Textured Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(2), 99–113 (1991)CrossRefGoogle Scholar
  12. 12.
    Pietikinen, M., Rosenfeld, A.: Image Segmentation Using Pyramid Node Linking. IEEE Transactions on Systems, Man and Cybernetics 12, 822–825 (1981)Google Scholar
  13. 13.
    Schroeter, P., Bigün, J.: Hierarchical Image Segmentation by Multi-dimensional Clustering and Orientation Adaptive Boundary Refinement. Pattern Recognition 28(5), 695–709 (1995)CrossRefGoogle Scholar
  14. 14.
    Rubio, T.J., Bandera, A., Urdiales, C., Sandoval, F.: A hierarchical context-based textured image segmentation algorithm for aerial images. In: Texture 2002 (2002),
  15. 15.
    Li, S.Z.: Markov Random Field Modeling in Computer Vision. Springer, Tokyo (1995)Google Scholar
  16. 16.
    Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  17. 17.
    Ozyildiz, E.: Adaptive texture and color segmentation for tracking moving objects. Master’s thesis, Pennsylvania State University (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ali Shahrokni
    • 1
  • Tom Drummond
    • 2
  • Pascal Fua
    • 1
  1. 1.Computer Vision LaboratoryEPFLLausanneSwitzerland
  2. 2.Department of EngineeringUniversity of CambridgeCambridge

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