Advertisement

Signal, Image and Video Processing

, Volume 10, Issue 4, pp 679–686 | Cite as

A simplified texture gradient method for improved image segmentation

  • Qi Wang
  • M. W. Spratling
Original Paper

Abstract

Inspired by the probability of boundary (Pb) algorithm, a simplified texture gradient method has been developed to locate texture boundaries within grayscale images. Despite considerable simplification, the proposed algorithm’s ability to locate texture boundaries is comparable with Pb’s texture boundary method. The proposed texture gradient method is also integrated with a biologically inspired model, to enable boundaries defined by discontinuities in both intensity and texture to be located. The combined algorithm outperforms the current state-of-art image segmentation method (Pb) when this method is also restricted to using only local cues of intensity and texture at a single scale.

Keywords

Cue integration Edge detection Image segmentation Texture segmentation 

References

  1. 1.
    Jain, K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. In: Proceedings IEEE International Conference on Systems, Man, and Cybernetics (1990)Google Scholar
  2. 2.
    Ojala, T., Pietikainen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognit. 32, 477–486 (1999)CrossRefGoogle Scholar
  3. 3.
    Will, S., Hermes, L., Buhmann, J.M.: On learning texture edge detectors. In: Proceedings of the International Conference on Image Processing (2000)Google Scholar
  4. 4.
    Scarpa, G., Gaetano, R., Haindl, M., Zerubia, J.: Hierarchical multiple Markov chain model for unsupervised texture segmentations. IEEE Trans. Image. Process. 18(8), 1830–1843 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)CrossRefGoogle Scholar
  6. 6.
    Ren, X.: Multi-scale improves boundary detection in natural images. In: Proceedings of European Conference on Computer Vision (2008)Google Scholar
  7. 7.
    Maire, M., Arbelaez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural image. In: IEEE Conference on Computer Vision and Recognition, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  8. 8.
    Shi, J., Malik, J.: Normalized cuts and image segmentations. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)CrossRefGoogle Scholar
  9. 9.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
  10. 10.
    Spratling, M.W.: Image segmentation using a sparse coding model of cortical area V1. IEEE Trans. Image Process. 22(4), 1631–1643 (2013)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–8(6), 679–698 (1986)CrossRefGoogle Scholar
  12. 12.
    Landy, M.S., Graham, N.: Visual perception of texture. In: Chalupa, L.M., Werner, J.S. (eds.) The Visual Neurosciences, pp. 1106–1118. MIT, Cambridge, MA (2004)Google Scholar
  13. 13.
    Ren, X., Bo, L.: Discriminative trained sparse code gradients for contour detection. In: Advances in Neural Information Processing System, pp. 584–592 (2012)Google Scholar
  14. 14.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 898–916 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Department of InformaticsKing’s College LondonLondonUK

Personalised recommendations