The Adaptive Edge Detection Algorithm Based on Nonseparable Sampling Morphological Wavelet

  • Ting Li
  • Wei Guo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 143)


Edge detection is the keytechnique in image processing, image analysis, and image pattern recognition. In this paper, we propose the kind of adaptive edge detection algorithmbased on nonseparable sampling morphological wavelets. First we choose the sampling matrix according to the interesting edge direction;then decomposethe original image by nonseparable morphological wavelets to get the high frequency data with different directions; after that set the low frequency parts to zero; finallywe can get the edge of the original image by reconstruction. The algorithm has two advantages: one is adaptivefor edge detection because the parameters could be chosenaccording to the interesting edge direction; the other isto avoid the edge excursion since using small convolution kernel.


Edge detection sampling morphological wavelets gray-scale morphology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhai, L., Dong, S., Ma, H.: Recent Methods and Applications on Image Edge Detection. In: International Workshop on Geoscience and Remote Sensing, pp. 332–335 (2008)Google Scholar
  2. 2.
    Li, X., Lv, X., et al.: The edge detection algorithm based on wavelet. In: INC 2010:6th International Conference on Networked Computing (2010)Google Scholar
  3. 3.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Patten Analysis and Machine Int. Plligence 8, 679–698 (1986)CrossRefGoogle Scholar
  4. 4.
    Wang, B., Fan, S.: An improved CANNY edge detection algorithm. In: 2009 Second International Workshop on Computer Science and Engineering, vol. 2, pp. 497–500 (2009)Google Scholar
  5. 5.
    Sweldens, W.: The Lifting Scheme: A New Philosophy in Biorthogonal Wavelet Constructions. In: Proc SPIE Wavelet Applications Signal Image Processing, vol. 259, p. 68279 (1995)Google Scholar
  6. 6.
    Karlsson, G., Vetterli, M.: Theory of Two-dimensional Multirate Filter Banks. IEEE Transaction on Acoustics and Signal Processing 38(6), 925–928 (1990)CrossRefGoogle Scholar
  7. 7.
    Goutsias, J., Heijmans, H.J.A.M.: Multiresolution Signal Decomposition Schemes, Part 2: Morphological Wavelets. IEEE Trans. on Image Processing 9, 1897–1913 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Heijmans, H.J.A.M., Goutsias, J.: Constructing Morphological Wavelets with the Lifting Scheme. In: Pattern Recognition and Information Processing, Proceedings of the Fifth International Conference on Pattern Recognition and Information Processing (PRIP 1999), Minsk, pp. 65–72 (1999)Google Scholar
  9. 9.
    Banghaln, J.A., Marshall, S.: Image and signal processing with mathematical morphology. Electronics & Communication Engineering, 117–128 (June 1998)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer EngineeringOrdnance Engineering CollegeShijiazhuangChina
  2. 2.College of ScienceHebei University of TechnologyTianjinChina
  3. 3.Mathematics and Information Science CollegeHebei Normal UniversityShijiazhuangChina

Personalised recommendations