http://iet.metastore.ingenta.com
1887

Fusion of intensity and inter-component chromatic difference for effective and robust colour edge detection

Fusion of intensity and inter-component chromatic difference for effective and robust colour edge detection

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Edge detection, especially from colour images, plays very important roles in many applications for image analysis, segmentation and recognition. Most existing methods extract colour edges via fusing edges detected from each colour components or detecting from the intensity image where inter-component information is ignored. In this study, an improved method on colour edge detection is proposed in which the significant advantage is the use of inter-component difference information for effective colour edge detection. For any given colour image C, a grey D-image is defined as the accumulative differences between each of its two colour components, and another grey R-image is then obtained by weighting of D-image and the grey intensity image G. The final edges are determined through fusion of edges extracted from R-image and G-image. Quantitative evaluations under various levels of Gaussian noise are achieved for further comparisons. Comprehensive results from different test images have proved that this approach outperforms edges detected from traditional colour spaces like RGB, YCbCr and HSV in terms of effectiveness and robustness.

References

    1. 1)
      • J.F. Canny . A computational approach to edge detection. IEEE T-PAMI , 6 , 679 - 698
    2. 2)
      • T. Gevers , H.M.G. Stokman . Classification of colour edges in video into shadow-geometry, highlight, or material transitions. IEEE T-Multimedia , 2 , 237 - 243
    3. 3)
    4. 4)
      • Rital, S., Cherifi, H.: `A combinatorial color edge detector', Proc. ICIAR, 2004, p. 289–297.
    5. 5)
      • J. van de Weijer , Th. Gevers , J.M. Geusebroek . Edge and corner detection by photometric quasi-invariants. IEEE T-PAMI , 4 , 625 - 630
    6. 6)
      • A. Fotinos , G. Economou , S. Fotopoulos . Use of relative entropy in colour edge detection. Electron. Lett. , 18 , 1532 - 1534
    7. 7)
      • H. Palus , S.J. Sangwine , R.E.N. Horne . (1998) Representation of colour images in different colour spaces.
    8. 8)
      • S.-Y. Zhu , K.N. Plataniotis , A.N. Venetsanopoulos . Comprehensive analysis of edge detection in colour image processing. J. Opt. Eng. , 4 , 612 - 625
    9. 9)
      • J.M. Geusebroek , R. van den Boomgaard , A.W.M. Smeulders , H. Geerts . Colour invariance. IEEE T-PAMI , 12 , 1338 - 1350
    10. 10)
      • Ruzon, M.A., Tomasi, C.: `Colour edge detection with the compass operator', Proc. CVPR, 1999, p. 160–166.
    11. 11)
      • Niu, L.-Q., Li, W.-J.: `Colour edge detection based on direction information measure', Proc. WCICA, 2006, p. 9533–9536.
    12. 12)
      • Tao, H., Huang, T.S.: `Colour image edge detection using cluster analysis', Proc. ICIP, 1997, p. 834–837.
    13. 13)
      • van de Weijer, J., Gevers, Th., Geusebroek, J.M.: `Colour edge detection by photometric quasi-invariants', Proc. ICCV, 2003, p. 1520–1525.
    14. 14)
      • Y. Hwang , J.S. Kim , I.S. Kweon . Change detection using a statistical model in an optimally selected color space. J. Comput. Vis. Image Understand. , 3 , 231 - 242
    15. 15)
      • A.N. Evans , X.U. Liu . A morphological gradient approach to color edge detection. IEEE Trans. Image Proc. , 6 , 1454 - 1463
    16. 16)
      • C. Zhou , B.W. Mel . Cue combination and color edge detection in natural scenes. J. Vis. , 4 , 1 - 25
    17. 17)
      • C. Theoharatos , G. Economou , S. Fotopoulos . Color edge detection using the minimal spanning tree. Pattern Recognit. , 4 , 603 - 606
    18. 18)
    19. 19)
      • H.-G. Choi , W.-C. Jung , J. Min , W.H. Lee , J.-W. Choi . Colour image detection by biomolecular photoreceptor using bacteriorhodopsin-based complex LB films. Biosensors Bioelectron. , 9 , 925 - 935
    20. 20)
      • Dikbas, S., Arid, T., Altunbasak, Y.: `Chrominance edge preserving grayscale transformation with approximate first principal component for color edge detection', Proc. ICIP, 2007, p. 261–264.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2009.0071
Loading

Related content

content/journals/10.1049/iet-ipr.2009.0071
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address