Classification of Photometric Factors Based on Photometric Linearization

  • Yasuhiro Mukaigawa
  • Yasunori Ishii
  • Takeshi Shakunaga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3852)


We propose a new method for classification of photometric factors, such as diffuse reflection, specular reflection, attached shadow, and cast shadow. For analyzing real images, we utilize the photometric linearization method which was originally proposed for image synthesis. First, we show that each pixel can be photometrically classified by the simple comparison of the pixel intensity. Our classification algorithm requires neither 3D shape information nor color information of the scene. Then, we show that the accuracy of the photometric linearization can be improved by introducing a new classification-based criterion to the linearization process. Experimental results show that photometric factors can be correctly classified without any special device.


Input Image Real Image Specular Reflection Linearization Process Base Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Woodham, R.J.: Photometric Stereo. MIT AI Memo (1978)Google Scholar
  2. 2.
    Shafer, S.: Using color to separate reflection components. Color Research and Applications 10, 210–218 (1985)CrossRefGoogle Scholar
  3. 3.
    Klinker, G., Shafer, S., Kanade, T.: The measurement of highlights in color images. IJCV 2(1), 7–32 (1988)CrossRefGoogle Scholar
  4. 4.
    Sato, Y., Ikeuchi, K.: Temporal-color space analysis of reflection. JOSA A 11(7), 2990–3002 (1994)CrossRefGoogle Scholar
  5. 5.
    Sato, Y., Wheeler, M., Ikeuchi, K.: Object Shape and Reflectance Modeling from Observation. In: Proc. SIGGRAPH 1997, pp. 379–387 (1997)Google Scholar
  6. 6.
    Wolff, L.B., Boult, E.: Constraining Object Features Using a Polarization Reflectance Model. IEEE Trans. PAMI 13(7), 635–657 (1991)Google Scholar
  7. 7.
    Nayar, S.K., Fang, X., Boult, T.E.: Removal of specularities using color and polarization. In: Proc. CVPR 1993, pp. 583–590 (1993)Google Scholar
  8. 8.
    Ikeuchi, K., Sato, K.: Determining Reflectance Properties of an Object Using Range and Brightness Images. IEEE Trans. PAMI 13(11), 1139–1153 (1991)Google Scholar
  9. 9.
    Shashua, A.: Geometry and Photometry in 3D Visual Recognition, Ph.D thesis, Dept. Brain and Cognitive Science, MIT (1992)Google Scholar
  10. 10.
    Belhumeur, P.N., Kriegman, D.J.: What is the Set of Images of an Object Under All Possible Lighting Conditions? In: Proc. CVPR 1996, pp. 270–277 (1996)Google Scholar
  11. 11.
    Georghiades, A.S., Kriegman, D.J., Belhumeur, P.N.: From Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose. IEEE Trans. PAMI 23(6), 643–660 (2001)Google Scholar
  12. 12.
    Mukaigawa, Y., Miyaki, H., Mihashi, S., Shakunaga, T.: Photometric Image-Based Rendering for Image Generation in Arbitrary Illumination. In: Proc. ICCV 2001, pp. 652–659 (2001)Google Scholar
  13. 13.
    Belhumeur, P.N., Kriegman, D.J., Yuille, A.L.: The bas-relief ambiguity. In: Proc. CVPR 1997, pp. 1060–1066 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yasuhiro Mukaigawa
    • 1
  • Yasunori Ishii
    • 2
  • Takeshi Shakunaga
    • 3
  1. 1.The Institute of Scientific and Industrial ResearchOsaka UniversityOsakaJapan
  2. 2.Matsushita Electric Industrial Co., Ltd 
  3. 3.Department of Computer ScienceOkayama UniversityOkayamaJapan

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