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Detection and Normalization of Blown-Out Illumination Areas in Grey-Scale Images

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Book cover Advances in Visual Computing (ISVC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7431))

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Abstract

This work presents a novel approach for blown-out illumination detection and normalization in grey-scale images. This situation takes place when the source of light is too close to the object or the object has high specularity. The presented algorithm calculates the image histogram and exploits it for further parameters calculation, which become a base for background detection algorithm, too bright pixel range definition, and finally the wrong lighting normalization. Examples of introduced algorithm performance are given.

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References

  1. Horn, B.K.P., Szeliski, R.S., Yuille, A.L.: Impossible shaded images. IEEE Trans. Pattern Anal. Mach. Intell. 15, 166–170 (1993)

    Article  Google Scholar 

  2. Ascher, U.M., Carter, P.M.: A multigrid method for shape from shading. Technical report, Vancouver, BC, Canada, Canada (1991)

    Google Scholar 

  3. Zheng, Q., Chellappa, R.: Estimation of illuminant direction, albedo, and shape from shading. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 680–702 (1991)

    Article  Google Scholar 

  4. Szeliski, R.: Fast shape from shading. In: Faugeras, O. (ed.) Computer Vision ECCV 1990. LNCS, vol. 427, pp. 359–368. Springer, Heidelberg (1990)

    Chapter  Google Scholar 

  5. Vogel, O., Breuß, M., Weickert, J.: Perspective Shape from Shading with Non-Lambertian Reflectance. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 517–526. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  6. Vogel, O., Breuß, M., Leichtweis, T., Weickert, J.: Fast shape from shading for phong-type surfaces. In: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2009, pp. 733–744. Springer, Heidelberg (2009)

    Google Scholar 

  7. Lei, Y., Jiu-Qiang, H.: A perspective shape-from-shading method using fast sweeping numerical scheme. Optica Applicata 38, 387–398 (2008)

    Google Scholar 

  8. Prados, E., Camilli, F.: A unifying and rigorous shape from shading method adapted to realistic data and applications. Journal of Mathematical Imaging and Vision, 307–328 (2006)

    Google Scholar 

  9. Durou, J.D., Falcone, M., Sagona, M.: Numerical methods for shape-from-shading: A new survey with benchmarks. Computer Vision and Image Understanding 109, 22–43 (2008)

    Article  Google Scholar 

  10. Withagen, P., Groen, R., Schutte, K.: Shadow detection using a physical basis. In: IEEE Conference Proceedings Instrumentation and Measurement Technology, pp. 119–124 (2008)

    Google Scholar 

  11. Kumar, S., Kaur, A.: Shadow detection and removal in colour images using matlab. International Journal of Engineering Science and Technology 2, 4482–4486 (2010)

    Google Scholar 

  12. Guo, R., Dai, Q., Hoiem, D.: Single-image shadow detection and removal using paired regions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pp. 2033–2040 (2011)

    Google Scholar 

  13. Rosin, P., Ellis, T.: Image difference threshold strategies and shadow detection. In: Proc. British Machine Vision Conf., pp. 347–356. BMVA Press (1995)

    Google Scholar 

  14. Joshi, A., Atev, S., Masoud, O., Papanikolopoulos, N.: Moving shadow detection with low- and mid-level reasoning. In: IEEE International Conference on Robotics and Automation, pp. 4827–4832 (2007)

    Google Scholar 

  15. Xie, X., Lam, K.: An efficient illumination normalization method for face recognition. Pattern Recognition Letters 27, 609–617 (2006)

    Article  Google Scholar 

  16. Chen, C.P., Chen, C.S.: Lighting normalization with generic intrinsic illumination subspace for face recognition. In: Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1089–1096 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Karolina Nurzyńska

  2. Institute of Medical Technology and Equipment, ul. Roosevelta 118, 41-800, Zabrze, Poland

    Ryszard Haraszczuk

Authors
  1. Karolina Nurzyńska
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  2. Ryszard Haraszczuk
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science, University of California at Irvine, 92697-3435, Irvine, CA

    Charless Fowlkes

  6. Eastman Kodak Company, 14650-2102, Rochester, NY, USA

    Sen Wang

  7. Department of Computer Science and Engineering, University of Colorado at Denver, 80217, Denver, CO, USA

    Min-Hyung Choi

  8. VRVis Zentrum für Virtual Reality and Visualisierung, 1220, Vienna, Austria

    Stephan Mantler

  9. California Institute for Telecommunications and Information Technology, University of California, 92093, San Diego, La Jolla, CA, USA

    Jürgen Schulze

  10. KAUST Visualizatioin Core Lab., 23955-6900, Thurwal, Saudi Arabia

    Daniel Acevedo

  11. Stony Brook University, 11794-4400, NY, USA

    Klaus Mueller

  12. Argonne National Laboratory, 60439, IL, USA

    Michael Papka

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© 2012 Springer-Verlag Berlin Heidelberg

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Nurzyńska, K., Haraszczuk, R. (2012). Detection and Normalization of Blown-Out Illumination Areas in Grey-Scale Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_28

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  • DOI: https://doi.org/10.1007/978-3-642-33179-4_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33178-7

  • Online ISBN: 978-3-642-33179-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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