Specular-based illumination estimation using blind signal separation techniques

Access Full Text

Specular-based illumination estimation using blind signal separation techniques

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.

Illumination estimation is important in many approaches to colour constancy, where object colour is measured without the effect of the spectral distribution of the illumination. Many illumination estimation methods for achieving colour constancy, particularly those based on the dichromatic reflection model, have performance limitations because they operate on images composed of blended specular and diffuse reflection components, and they may require image segmentation into regions; segmentation is a well-known image-processing challenge. This study proposes an illumination estimation method, which uses constrained blind signal separation anchored on the dichromatic reflection model, connected to a linear model of the illumination spectrum. Unlike conventional methods that use mixed-image components, the proposed method uses a specular image component extracted explicitly by blind signal separation. This can yield better illumination estimates, and blind signal separation can avoid image segmentation problems. Results of experiments show that the proposed method can recover the illumination spectral distribution, and that the extracted specular component yields better illumination estimation than mixed components. Similar results were observed for the two blind signal separation techniques assessed in this study; namely, the spatially constrained FastICA and independent component analysis based on mutual information.

Inspec keywords: image colour analysis; blind source separation; image segmentation; independent component analysis

Other keywords: constrained blind signal separation; linear model; specular-based illumination estimation; specular image component; independent component analysis; image segmentation; illumination spectrum; diffuse reflection component; blind signal separation technique; blended specular reflection component; object colour measurement; spatially constrained FastICA; colour constancy; dichromatic reflection model

Subjects: Other topics in statistics; Optical, image and video signal processing; Other topics in statistics; Computer vision and image processing techniques

References

    1. 1)
      • Shrestha, R., Hardeberg, J.Y.: `Computational color constancy using a stereo camera', Sixth European Conf. Colour in Graphics, Imaging, and Vision (CGIV), 2012, 1, p. 69–74.
    2. 2)
    3. 3)
      • Finlayson, G., Schaefer, G.: `Convex and non-convex illuminant constraints for dichromatic colour constancy', Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2001, 1, p. 598–604.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • N. Das , A. Routray , P. Dash , D. India . Ica methods for blind source separation of instantaneous mixtures: a case study. Neural Inf. Process., Lett. Rev. , 11 , 225 - 246
    8. 8)
    9. 9)
    10. 10)
      • A. Cichocki , S.i. Amari . (2003) Adaptive blind signal and image processing: learing algorithm and applications.
    11. 11)
    12. 12)
    13. 13)
      • E. Huizingh . (2007) Applied Statistics with SPSS.
    14. 14)
    15. 15)
      • Yonghong, L., Haixia, L., Aichun, Y.: `Color recovering based on dichromatic reflection model and finite dimensional linear model', Proc. Int. Conf. Measuring Technology and Mechatronics Automation, April 2009, 1, p. 441–444.
    16. 16)
    17. 17)
    18. 18)
      • Tan, R.T.: `Illumination color and intrinsic surface properties-physics-based color analyses from a single image', 2003, PhD, The University of Tokyo.
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • Kino flo lighting systems, ‘true match lamps spectral charts’. Available at http:// www.kinoflo.com/Kino.
    23. 23)
      • The imaging source, ‘spectral sensitivity and color formats’. Available at http://www.theimagingsource.com.
    24. 24)
    25. 25)
    26. 26)
    27. 27)
      • Gijsenij, A., Gevers, T.: `Color constancy using image regions', Proc. IEEE Int. Conf. Image Processing, October 2007, 3, p. 501–504.
    28. 28)
    29. 29)
    30. 30)
    31. 31)
      • Yao, L.: `Estimation illumination chromaticity', Proc. Second Int. Symp. Intelligent Information Technology Application, 2008, 3, p. 756–760.
    32. 32)
    33. 33)
      • Kwon, O., Cho, Y., Kim, Y., Ha, Y.: `Illumination estimation based on valid pixel selection in highlight region', Proc. Int. Conf. Image Processing, 2004, 4, p. 2419–2422.
    34. 34)
    35. 35)
    36. 36)
    37. 37)
    38. 38)
    39. 39)
      • Schaefer, G.: `Robust dichromatic colour constancy', Proc. Int. Conf. Image Analysis and Recognition, 2004, 3212, p. 257–264.
    40. 40)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2011.0376
Loading

Related content

content/journals/10.1049/iet-ipr.2011.0376
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading