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Tone Correction with Dynamic Objects for Seamless Image Mosaic

  • Yong-Ho Shin
  • Min-Gyu Park
  • Young-Sun Jeon
  • Young-Su Moon
  • Shi-Hwa Lee
  • Kuk-Jin Yoon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)

Abstract

This paper presents a tone compensation method between images to make a seamless panoramic image. Different camera settings of input images, including white-balance, exposure time, and f-stops, affect the overall color tone of a resultant panoramic image. Although numerous methods have been proposed to deal with such color variations for seamless image stitching, most of them do not properly consider the dynamic scene in which different scene contents exist in input images. In this paper, we propose an efficient method that takes dynamic scene contents into account for compensating color tone difference. The proposed approach consists of three steps. First, we compensate the color tone difference by using the linear color transform with robust local features. Second, we filter out dynamic objects (i.e., dynamic scene contents) by measuring similarity between the linear transformed image and the reference image. Finally, we precisely correct the color variation with detected consistent regions only. The qualitative evaluation shows superior or competitive results compared to commercially available products.

Keywords

Panorama Tone correction Seamless image mosaic 

References

  1. 1.
    Szeliski, R.: Image mosaicing for tele-reality applications. In: IEEE Workshop on Applications of Computer Vision, pp. 44–53 (1994)Google Scholar
  2. 2.
    Szeliski, R., Corporation, M.: Video mosaics for virtual environments. IEEE Computer Graphics and Applications 16, 22–30 (1996)CrossRefGoogle Scholar
  3. 3.
    Shum, H.Y., Szeliski, R.: Construction of panoramic mosaics with global and local alignment. International Journal of Computer Vision 36, 101–130 (2000)CrossRefGoogle Scholar
  4. 4.
    Zoghlami, I., Faugeras, O., Deriche, R., Antipolis Cedex, F.S.: Using geometric corners to build a 2d mosaic from a set of images. In: CVPR 1997, pp. 420–425 (1997)Google Scholar
  5. 5.
    Mclauchlan, P.F., Jaenicke, A., Xh, G.G.: Image mosaicing using sequential bundle adjustment. In: Proc. BMVC, pp. 751–759 (2000)Google Scholar
  6. 6.
    Brown, M., Lowe, D.: Automatic panoramic image stitching using invariant features. International Journal of Computer Vision 74, 59–73 (2007)CrossRefGoogle Scholar
  7. 7.
    Burt, J., Adelson, H.: A multiresolution spline with application to image mosaics. ACM Transactions on Graphics 2, 217–236 (1983)CrossRefGoogle Scholar
  8. 8.
    Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Trans. Graph. 22, 313–318 (2003)CrossRefGoogle Scholar
  9. 9.
    Davis, J.: Mosaics of scenes with moving objects. In: CVPR 1998: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 354. IEEE Computer Society, Washington, DC (1998)Google Scholar
  10. 10.
    Uyttendaele, M., Eden, A., Szeliski, R.: Eliminating ghosting and exposure artifacts in image mosaics. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. 509 (2001)Google Scholar
  11. 11.
    Mills, A., Dudek, G.: Image stitching with dynamic elements. Image and Vision Computing 27, 1593–1602 (2009)CrossRefGoogle Scholar
  12. 12.
    Zheng, L., Zhang, J., Luo, Y.: Color matching in colour remote sensing image. In: IMSCCS 2006: Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences, vol. 1, pp. 303–306. IEEE Computer Society, Washington, DC (2006)Google Scholar
  13. 13.
    Azzari, P., Bevilacqua, A.: Joint Spatial and Tonal Mosaic Alignment for Motion Detection with PTZ Camera. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2006. LNCS, vol. 4142, pp. 764–775. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Tian, G.Y., Gledhill, D., Taylor, D., Clarke, D.: Colour correction for panoramic imaging. In: Proceedings of the Sixth International Conference on Information Visualisation, pp. 483–488. IEEE Computer Society, Los Alamitos (2002)CrossRefGoogle Scholar
  15. 15.
    Goldman, D.B.: hung Chen, J.: Vignette and exposure calibration and compensation. In: ICCV 2005: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 1, pp. 899–906. IEEE Computer Society (2005)Google Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91 (2004)CrossRefGoogle Scholar
  17. 17.
    Finlayson, G.D., Drew, M.S., Funt, B.V.: Color constancy: Generalized diagonal transforms suffice. J. Opt. Soc. Am. A 11, 3011–3020 (1994)CrossRefGoogle Scholar
  18. 18.
    Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Computer Graphics and Applications 21, 34–41 (2001)CrossRefGoogle Scholar
  19. 19.
    Ruderman, L., Cronin, W., Chiao, C.C.: Statistics of cone responses to natural images: Implications for visual coding. Journal of the Optical Society of America A 15, 2036–2045 (1998)CrossRefGoogle Scholar
  20. 20.
  21. 21.
  22. 22.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yong-Ho Shin
    • 1
  • Min-Gyu Park
    • 1
  • Young-Sun Jeon
    • 2
  • Young-Su Moon
    • 2
  • Shi-Hwa Lee
    • 2
  • Kuk-Jin Yoon
    • 1
  1. 1.Gwangju Institute of Science and TechnologyBuk-guRepublic of Korea
  2. 2.Samsung Advanced Institute of TechnologyYongin-SiRepublic of Korea

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