Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
Eberly, D., Gardner, R., Morse, B., Pizer, S., Scharlach, C.: Ridges for image analysis. Journal of Mathematical Imaging and Vision 4, 353–373 (1994)
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)
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)
Hoffman, R., Jain, A.K.: Segmentation and classification of range images. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(5), 608–620 (1987)
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)
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)
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)
Chandra, S.: Analysis of retinal angiogram images. M.S. Thesis, Centre for Visual Information Technology, IIIT Hyderabad, India (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sivaswamy, J., Joshi, G.D., Chandra, S. (2006). An Alternative Curvature Measure for Topographic Feature Detection. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_21
Download citation
DOI: https://doi.org/10.1007/11949619_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68301-8
Online ISBN: 978-3-540-68302-5
eBook Packages: Computer ScienceComputer Science (R0)