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Supervised Scale-Invariant Segmentation (and Detection)

  • Conference paper
Scale Space and Variational Methods in Computer Vision (SSVM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6667))

Abstract

The scale-invariant detection of image structure has been a topic of study within computer vision and image analysis since long. To date, Lindeberg’s scale selection method has probably been the most fruitful and successful approach to this problem. It provides a general technique to cope with the detection of structures over scale that can be successfully expressed in terms of Gaussian differential operators. Any detection or segmentation task would potentially benefit from a similar approach to deal with scale. For many of the real-world image structures of interest, however, it will often be impossible to explicitly design or handcraft an operator that is capable of detecting them in a sensitive and specific way. In this paper, we present an approach to the scale-selection problem in which the construction of the detector is driven by supervised learning techniques. The resulting classification method is designed so as to achieve scale-invariance and may be thought of as a supervised version of Lindeberg’s classical scheme.

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References

  1. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  2. Bishop, C.: Pattern recognition and machine learning. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  3. Brodatz, P.: Textures: A Photographic Album for Artists & Designers. Dover, New York (1966)

    Google Scholar 

  4. Duda, R., Hart, P., Stork, D.: Pattern classification, vol. 2. Wiley, Chichester (2001)

    MATH  Google Scholar 

  5. Duin, R., Tax, D.: Classifier conditional posterior probabilities. In: Advances in Pattern Recognition, pp. 611–619 (1998)

    Google Scholar 

  6. Florack, L., Ter Haar Romeny, B., Viergever, M., Koenderink, J.: The Gaussian scale-space paradigm and the multiscale local jet. International Journal of Computer Vision 18(1), 61–75 (1996)

    Article  Google Scholar 

  7. Florack, L.: Image Structure. Kluwer Academic Publishers, Dordrecht (1997)

    Book  Google Scholar 

  8. Florack, L., ter Haar Romeny, B., Koenderink, J., Viergever, M.: Scale and the differential structure of images. Image and Vision Computing 10(6), 376–388 (1992)

    Article  MATH  Google Scholar 

  9. Folkesson, J., et al.: Segmenting articular cartilage automatically using a voxel classification approach. IEEE Trans. on Medical Imaging 26(1), 106–115 (2007)

    Article  Google Scholar 

  10. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  11. van Ginneken, B., Stegmann, M., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Medical Image Analysis 10(1), 19–40 (2006)

    Article  Google Scholar 

  12. Hjelmås, E., Low, B.: Face detection: A survey. Computer Vision and Image Understanding 83(3), 236–274 (2001)

    Article  MATH  Google Scholar 

  13. Janssen, J., et al.: Scale-invariant segmentation of dynamic contrast-enhanced perfusion MR images with inherent scale selection. J. Visualization and Computer Animation 13(1), 1–19 (2002)

    Article  MATH  Google Scholar 

  14. Kang, Y., Morooka, K., Nagahashi, H.: Scale invariant texture analysis using multi-scale local autocorrelation features. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds.) Scale-Space 2005. LNCS, vol. 3459, pp. 363–373. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  15. Kokkinos, I., Yuille, A.: Scale invariance without scale selection. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8. IEEE, Los Alamitos (2008)

    Google Scholar 

  16. Leung, M., Peterson, A.: Scale and rotation invariant texture classification. In: The 26th Asilomar Conference on Signals, Systems and Computers, pp. 461–465 (1992)

    Google Scholar 

  17. Lindeberg, T.: Scale-Space Theory in Computer Vision. Kluwer Academic, Dordrecht (1994)

    Book  MATH  Google Scholar 

  18. Lindeberg, T.: Feature detection with automatic scale selection. Int. J. of Computer Vision 30(2), 79–116 (1998)

    Article  Google Scholar 

  19. Loog, M., Ginneken, B.: Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification. IEEE Trans. on Medical Imaging 25(5), 602–611 (2006)

    Article  Google Scholar 

  20. Loog, M., Li, Y., Tax, D.M.J.: Maximum Membership Scale Selection. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 468–477. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  21. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)

    Article  Google Scholar 

  22. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Computer Vision 60(1), 63–86 (2004)

    Article  Google Scholar 

  23. Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth International Conference on Computer Vision, pp. 555–562. IEEE, Los Alamitos (2002)

    Google Scholar 

  24. Platel, B., Kanters, F., Florack, L., Balmachnova, E.: Using multiscale top points in image matching. In: International Conference on Image Processing, ICIP 2004, vol. 1, pp. 389–392. IEEE, Los Alamitos (2005)

    Google Scholar 

  25. Pun, C., Lee, M.: Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans. PAMI, 590–603 (2003)

    Google Scholar 

  26. Shotton, J., Blake, A., Cipolla, R.: Contour-based learning for object detection. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 503–510. IEEE, Los Alamitos (2005)

    Google Scholar 

  27. Ter Haar Romeny, B.: Front-End Vision and Multi-Scale Image Analysis. Kluwer Academic, Dordrecht (2002)

    MATH  Google Scholar 

  28. Yang, M., Kriegman, D., Ahuja, N.: Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Pattern Recognition Laboratory, Delft University of Technology, The Netherlands

    Yan Li, David M. J. Tax & Marco Loog

Authors
  1. Yan Li
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  2. David M. J. Tax
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  3. Marco Loog
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Editor information

Editors and Affiliations

  1. Computer Science Department, Technion — Israel Institute of Technology, 718 Taub Building Technion, 32000, Haifa, Israel

    Alfred M. Bruckstein

  2. Department of Biomedical Engineering, Biomedical Image Analysis and Interpretation, Eindhoven University of Technology, Den Dolech 2 – WH 2.101, 5600 MB, Eindhoven, The Netherlands

    Bart M. ter Haar Romeny

  3. Faculaty of Engineering, School of Electrical Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Alexander M. Bronstein

  4. Institute of Computationa Science, Faculty of Informatics SI-109, UniversitĂ  della Svizzera Italiana, Via Giuseppe Buffi 13, 6904, Lugano, Switzerland

    Michael M. Bronstein

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, Y., Tax, D.M.J., Loog, M. (2012). Supervised Scale-Invariant Segmentation (and Detection). In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_30

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  • DOI: https://doi.org/10.1007/978-3-642-24785-9_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24784-2

  • Online ISBN: 978-3-642-24785-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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