Advertisement

Detecting Regions from Single Scale Edges

  • Konstantinos Rapantzikos
  • Yannis Avrithis
  • Stefanos Kollias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6553)

Abstract

We believe that the potential of edges in local feature detection has not been fully exploited and therefore propose a detector that starts from single scale edges and produces reliable and interpretable blob-like regions and groups of regions of arbitrary shape. The detector is based on merging local maxima of the distance transform guided by the gradient strength of the surrounding edges. Repeatability and matching score are evaluated and compared to state-of-the-art detectors on standard benchmarks. Furthermore, we demonstrate the potential application of our method to wide-baseline matching and feature detection in sequences involving human activity.

Keywords

Maximally Stable Extremal Region Salient Region Detector Edge Fragment Euclidean Distance Transform Spurious Edge 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision 3, 177–280 (2007)CrossRefGoogle Scholar
  2. 2.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. IJCV 65, 43–72 (2005)CrossRefGoogle Scholar
  3. 3.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing 22, 761–767 (2004)CrossRefGoogle Scholar
  4. 4.
    Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. IJCV 60, 63–86 (2004)CrossRefGoogle Scholar
  5. 5.
    Fan, S., Ferrie, F.: Structure guided salient region detector. In: BMVC (2008)Google Scholar
  6. 6.
    Perdoch, M., Matas, J., Obdrzalek, S.: Stable affine frames on isophotes. In: ICCV, vol. 104 (2007)Google Scholar
  7. 7.
    Tuytelaars, T., Van Gool, L.: Content-Based Image Retrieval Based on Local Affinely Invariant Regions. In: Huijsmans, D.P., Smeulders, A.W.M. (eds.) VISUAL 1999. LNCS, vol. 1614, pp. 493–500. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Lindeberg, T.: Feature detection with automatic scale selection. IJCV 30, 79–116 (1998)CrossRefGoogle Scholar
  9. 9.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. IJCV 30, 117–154 (1998)CrossRefGoogle Scholar
  11. 11.
    Tuytelaars, T., Van Gool, L.: Wide baseline stereo matching based on local, affinely invariant regions. In: BMVC (2000)Google Scholar
  12. 12.
    Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. IJCV 59, 167–181 (2004)CrossRefGoogle Scholar
  13. 13.
    Lindeberg, T., Garding, J.: Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure. Image and Vision Computing 15, 415–434 (1997)CrossRefGoogle Scholar
  14. 14.
    Collins, R.T., Ge, W.: CSDD Features: Center-Surround Distribution Distance for Feature Extraction and Matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 140–153. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Agrawal, M., Konolige, K., Blas, M.R.: CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 102–115. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Kadir, T., Zisserman, A., Brady, M.: An Affine Invariant Salient Region Detector. In: Pajdla, T., Matas, J. (eds.) ECCV 2004, Part I. LNCS, vol. 3021, pp. 228–241. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Brown, M., Lowe, D.: Invariant features from interest point groups. In: BMVC (2002)Google Scholar
  19. 19.
    Lazebnik, S., Schmid, C., Ponce, J.: Semi-local affine parts for object recognition. In: BMVC (2004)Google Scholar
  20. 20.
    Agarwal, A., Triggs, B.: Hyperfeatures – Multilevel Local Coding for Visual Recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 30–43. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Koniusz, P., Mikolajczyk, K.: Segmentation based interest points and evaluation of unsupervised image segmentation methods. In: BMVC (2009)Google Scholar
  22. 22.
    Russell, B., Efros, A., Sivic, J., Freeman, W., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: CVPR (2006)Google Scholar
  23. 23.
    Canny, J.: A computational approach to edge detection. PAMI 8, 679–698 (1986)CrossRefGoogle Scholar
  24. 24.
    Felzenszwalb, P., Huttenlocher, D.: Distance transforms of sampled functions. Technical report (2004)Google Scholar
  25. 25.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)CrossRefGoogle Scholar
  26. 26.
    Rapantzikos, K., Avrithis, Y., Kollias, S.: Dense saliency-based spatiotemporal feature points for action recognition. In: CVPR 2009 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Konstantinos Rapantzikos
    • 1
  • Yannis Avrithis
    • 1
  • Stefanos Kollias
    • 1
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensGreece

Personalised recommendations