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Adaptive Structuring Elements Based on Salience Information

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7594))

Abstract

Adaptive structuring elements modify their shape and size according to the image content and may outperform fixed structuring elements. Without any restrictions, they suffer from a high computational complexity, which is often higher than linear with respect to the number of pixels in the image. This paper introduces adaptive structuring elements that have predefined shape, but where the size is adjusted to the local image structures. The size of adaptive structuring elements is determined by the salience map that corresponds to the salience of the edges in the image, which can be computed in linear time. We illustrate the difference between the new adaptive structuring elements and morphological amoebas. As an example of its usefulness, we show how the new adaptive morphological operations can isolate the text in historical documents.

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References

  1. Matheron, G.: Random Sets and Integral Geometry. Willey, New York (1975)

    MATH  Google Scholar 

  2. Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1982)

    MATH  Google Scholar 

  3. Cuisenaire, O.: Locally adaptable mathematical morphology using distance transform. Pattern Recognition 39(3), 405–416 (2006)

    Article  MATH  Google Scholar 

  4. Shih, F., Cheng, S.: Adaptive mathematical morphology for edge linking. Information Sciences 167, 9–21 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  5. Lerallut, R., Decencière, E., Meyer, F.: Image processing using morphological amoebas. In: Proc. of International Symposium on Mathematical Morphology, pp. 13–25 (2005)

    Google Scholar 

  6. Grazzini, J., Soille, P.: Edge-preserving smoothing using a similarity measure in adaptive geodesic neighbourhoods. Pattern Recognition 42(10), 2306–2316 (2009)

    Article  MATH  Google Scholar 

  7. Debayle, J., Pinoli, J.: Spatially Adaptive Morphological Image Filtering using Intrinsic Structuring Elements. Image Analysis and Stereology 24(3), 145–158 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  8. Verdú-Monedero, R., Angulo, J., Serra, J.: Anisotropic morphological filters with spatially-variant structuring elements based on image-dependent gradient fields. IEEE Transactions on Image Processing 20(1), 200–212 (2011)

    Article  MathSciNet  Google Scholar 

  9. Angulo, J.: Morphological Bilateral Filtering and Spatially-Variant Structuring Functions. In: Proc. of International Symposium on Mathematical Morphology, pp. 212–223 (2011)

    Google Scholar 

  10. Dokládal, P., Dokládalová, E.: Grey-Scale Morphology with Spatially-Variant Rectangles in Linear Time. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 674–685. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Bouaynaya, N., Charif-Chefchaouni, M., Schonfeld, D.: Theoretical Foundation of Spatially-Variant Mathematical Morphology Part I: Binary Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(5), 823–836 (2008)

    Article  Google Scholar 

  12. Bouaynaya, N., Schonfeld, D.: Theoretical Foundation of Spatially-Variant Mathematical Morphology Part II: Gray-Level Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(5), 837–850 (2008)

    Article  Google Scholar 

  13. Roerdink, J.: Adaptive and group invariance in mathematical morphology. In: Proc. of IEEE International Conference on Image Processing, pp. 2253–2256 (2009)

    Google Scholar 

  14. Maragos, P.A., Vachier, C.: Overview of adaptive morphology: Trends and perspectives. In: Proc. of IEEE Int. Conference on Image Processing, pp. 2241–2244 (2009)

    Google Scholar 

  15. Rosin, P., West, G.: Salience distance transforms. CVGIP: Graphical Models and Image Processing 57(6), 483–521 (1995)

    MATH  Google Scholar 

  16. Ikonen, L.: Distance Transform on Gray Level Surfaces. PhD thesis, Lappeeranta University of Technology, Lappeeranta, Finland (2006)

    Google Scholar 

  17. Borgefors, G.: Distance transformations in digital images. Computer Vision, Graphics and Image Processing 34, 344–371 (1986)

    Article  Google Scholar 

  18. Rosin, P.: A simple method for detecting salient regions. Pattern Recognition 42(11), 2363–2371 (2009)

    Article  MATH  Google Scholar 

  19. Canny, J.: A Computational Approach To Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Ćurić, V., Luengo Hendriks, C.L. (2012). Adaptive Structuring Elements Based on Salience Information. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_39

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  • DOI: https://doi.org/10.1007/978-3-642-33564-8_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33563-1

  • Online ISBN: 978-3-642-33564-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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