An Alternative Curvature Measure for Topographic Feature Detection

  • Jayanthi Sivaswamy
  • Gopal Datt Joshi
  • Siva Chandra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


The notion of topographic features like ridges, trenches, hills, etc. is formed by visualising the 2D image function as a surface in 3D space. Hence, properties of such a surface can be used to detect features from images. One such property, the curvature of the image surface, can be used to detect features characterised by a sharp bend in the surface. Curvature based feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope.


Feature Detection Image Surface Curvature Measure Medial Point Crest Line 
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.


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  1. 1.
    Lopez, A., Lumbreras, F., Serrat, J., Villanueva, J.: Evaluation of methods for ridge and valley detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(4), 327–335 (1999)CrossRefGoogle Scholar
  2. 2.
    Maintz, J.B.A., van den Elsen, P.A., Viergever, M.A.: Evaluation of ridge seeking operators for multimodality medical image matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(4), 353–356 (1996)CrossRefGoogle Scholar
  3. 3.
    Eberly, D., Gardner, R., Morse, B., Pizer, S., Scharlach, C.: Ridges for image analysis. Journal of Mathematical Imaging and Vision 4, 353–373 (1994)CrossRefGoogle Scholar
  4. 4.
    Monga, O., Armande, N., Montesinos, P.: Thin nets and crest lines: Applications to satellite data and medical images. In: Proc. IEEE Conference of Image Processing, vol. 2, pp. 468–471 (1995)Google Scholar
  5. 5.
    Fan, T., Medioni, G., Nevatia, R.: Description of surfaces from range data using curvature properties. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 86–91 (1986)Google Scholar
  6. 6.
    Hoffman, R., Jain, A.K.: Segmentation and classification of range images. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(5), 608–620 (1987)CrossRefGoogle Scholar
  7. 7.
    Flynn, P.J., Jain, A.K.: On reliable curvature estimation. In: Proc. of the International Conference on Computer Vision and Pattern Recognition, pp. 110–116 (1989)Google Scholar
  8. 8.
    Chandra, S., Sivaswamy, J.: An analysis of curvature based ridge and valley detection. In: Proc. of International conference on Acoustics speech and signal processing (ICASSP) (2006)Google Scholar
  9. 9.
    Tupin, F., Maitre, H., Margin, J.F., Nicolars, J.M., Pechersky, E.: Detection of linear features in sar images: Application to road network extraction. IEEE Transactions on Geoscience and Remote Sensing 36, 434–453 (1998)CrossRefGoogle Scholar
  10. 10.
    Chandra, S.: Analysis of retinal angiogram images. M.S. Thesis, Centre for Visual Information Technology, IIIT Hyderabad, India (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jayanthi Sivaswamy
    • 1
  • Gopal Datt Joshi
    • 1
  • Siva Chandra
    • 1
  1. 1.Centre for Visual Information TechnologyIIIT HyderabadIndia

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