Abstract

Photometric-stereo techniques are based on the fact that image intensity depends upon the orientation of the surface with regard to the source of the illumination and its spectral reflectance. They are of special interest when dealing with rough surfaces because they usually present shadowed regions where sudden illumination changes might be found. In the present work we introduce an extension of the four-source photometric-stereo algorithm to color images that is able to recover the surface spectral reflectance of objects captured with a red–green–blue (RGB) camera. This method allows image rendering, even for rough-textured surfaces, under different directions of the impinging illumination. In addition, the introduction of spectral recovery techniques applied to the albedo and spectral reflectance from rough surfaces offers the possibility of image rendering for scenes captured under sources of illumination differing in spectral distribution. Using albedo instead of RGB information helps to avoid any shadows or highlights that might falsify results. One of the advantages of this spectral-based photometric-stereo method is that it can recover not only the albedo values, but also the spectral reflectance spectrum of an object’s surface on a pixel-by-pixel basis, as can be done with more complex hyperspectral imaging devices involving a camera coupled to an extensive set of narrowband filters.

© 2009 Optical Society of America

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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
  5. E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color Res. Appl. 32, 352-360 (2007).
    [CrossRef]
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    [CrossRef]
  7. J. L. Nieves, E. M. Valero, S. M. C. Nascimento, J. Hernández-Andrés, and J. Romero “Multispectral synthesis of daylight using a commercial digital CCD camera,” Appl. Opt. 44, 5696-5703 (2005).
    [CrossRef] [PubMed]
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    [CrossRef]
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    [CrossRef]
  16. B. Kim and P. Burguer, “Depth and shape from shading using the photometric stereo method,” Comp. Vis. Graph. Image Process. 54, 416-427 (1991).
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
  22. G. Healey and L. Wang, “Three-dimensional surface segmentation using multicolored illumination,” Opt. Eng. 37, 1553-1562 (1998).
    [CrossRef]
  23. C. Hernández, G. Vogiatzis, G. J. Brostow, B. Stengar, and R. Cipolla, “Non-rigid photometric stereo with colored lights,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.
  24. M. Chandraker, S. Agarwal, and D. Kriegman, “ShadowCuts: photometric stereo with shadows,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8.
  25. C. Hernández, Vogiatzis, and R. Cipolla, “Shadows in three-source photometric stereo,” in Proceedings of IEEE European Conference on Computer Vision (IEEE, 2008), pp. 1-14.
  26. K. Schl and O. Witting, “Photometric stereo for non-Lambertian surfaces using color information,” in Proceedings of 5th International Conference on Computer Analysis of Images and Patterns (Springer, 1993), pp. 444-451.
  27. L. L. Kontsevich, A. P. Petrov, and I. S. Vergelskaya, “Reconstruction of shape from shading in color images,” J. Opt. Soc. Am. A 11, 1047-1052 (1994).
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  28. M. S. Drew, “Shape from color,” Technical Report CSS/LCCR TR 92-07 (Simon Fraser University School of Computing Science, 1992).
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    [CrossRef]
  30. P. H. Christensen and L. G. Shapiro “Three dimensional shape from color photometric stereo,” Int. J. Comput. Vis. 13, 213-227 (1994).
    [CrossRef]
  31. S. Barsky and M. Petrou, “Color photometric stereo: Simultaneous reconstruction of local gradient and color of rough textured surfaces,” in Proceedings of Eighth IEEE International Conference on Computer Vision (IEEE, 2001), pp. 600-605.
    [CrossRef]
  32. B. Bringier, D. Helbert, and M. Khoudeir, “Photometric reconstruction of a dynamic textured surface from just one color image acquisition,” J. Opt. Soc. Am. 25, 566-574 (2008).
    [CrossRef]
  33. C.-Yen Chen, R. Klette, and C.-F. Chen, “Recovery of colored surface reflectances using the photometric stereo method,” in Proceedings of International Conference on Information Systems (Association for Information Systems, 2003), pp. 969-974.
  34. M. de Lasarte, J. Pujol, M. Arjona, and M. Vilaseca, “Influence of the size of the training set on color measurements performed using a multispectral imaging system,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 437-440.
  35. C. Plata, E. M. Valero, J. L. Nieves, and J. Romero, “Supervised training sample selection for the estimation of spectral reflectance using a RGB camera,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 519-522.
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    [CrossRef]

2008 (2)

C. Hernández, G. Vogiatzis, and R. Cipolla, “Multiview photometric stereo,” IEEE Trans. Pattern Anal. Machine Intell. 30, 548-554 (2008).
[CrossRef]

B. Bringier, D. Helbert, and M. Khoudeir, “Photometric reconstruction of a dynamic textured surface from just one color image acquisition,” J. Opt. Soc. Am. 25, 566-574 (2008).
[CrossRef]

2007 (1)

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color Res. Appl. 32, 352-360 (2007).
[CrossRef]

2006 (1)

21. T.-P. Wu, K.-L. Tang, and T.-T. Wong, “Dense photometric stereo: a Markov random field approach,” IEEE Trans. Pattern Anal. Machine Intell. 28, 1830-1846 (2006).
[CrossRef]

2005 (2)

2003 (2)

S. Barsky and M. Petrou, “The 4-source photometric stereo technique for 3-dimensional surfaces in the presence of highlights and shadows,” IEEE Trans. Pattern Anal. Machine Intell. 25, 1239-1252 (2003).
[CrossRef]

G. McGunnigle and M. Chantler, “Rough surface description using photometric stereo,” Meas. Sci. Technol. 14, 699-709 (2003).
[CrossRef]

2002 (2)

J. Hardeberg, F. Schmidtt, H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41, 2532-2548 (2002).
[CrossRef]

S. M. C. Nascimento, F. P. Ferreira, and D. H. Foster, “Statistics of spatial cone excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484-1490 (2002).
[CrossRef]

2000 (1)

Corbalan, Millan, and Yzuel, “Color measurement in standard CIELab coordinates using a 3CCD camera: correction for the influence of the light source,” Opt. Eng. 39, 1470-1476(2000).
[CrossRef]

1998 (1)

G. Healey and L. Wang, “Three-dimensional surface segmentation using multicolored illumination,” Opt. Eng. 37, 1553-1562 (1998).
[CrossRef]

1997 (1)

M. S. Drew, “Photometric stereo without multiple images,” Proc. SPIE 3016, 369-380 (1997).
[CrossRef]

1994 (3)

M. Oren and S. K. Nayar, “Generalization of Lambert's reflectance model,” Comp. Graph. 28, 239-246 (1994).
[CrossRef]

P. H. Christensen and L. G. Shapiro “Three dimensional shape from color photometric stereo,” Int. J. Comput. Vis. 13, 213-227 (1994).
[CrossRef]

L. L. Kontsevich, A. P. Petrov, and I. S. Vergelskaya, “Reconstruction of shape from shading in color images,” J. Opt. Soc. Am. A 11, 1047-1052 (1994).
[CrossRef]

1993 (1)

M. S. Drew, “Optimization approach to dichromatic images,” J. Math. Imaging Vis. 3, 187 (1993).
[CrossRef]

1991 (2)

H. Tagare and R. de Figuiredo, “A theory of photometric stereo for a class of diffuse non-Lambertian surfaces,” IEEE Trans. Pattern Anal. Machine Intell. 13, 133-152 (1991).
[CrossRef]

B. Kim and P. Burguer, “Depth and shape from shading using the photometric stereo method,” Comp. Vis. Graph. Image Process. 54, 416-427 (1991).

1987 (1)

B. A. Wandell, “The synthesis and analysis of color images,” IEEE Trans. Pattern Anal. Machine Intell. pami-9, 2-13 (1987).
[CrossRef]

1982 (1)

E. Coleman Jr. and R. Jain, “Obtaining 3-Dimensional shape of textured and specular surfaces using four-source photometry,” Comp. Graph. Image Process. 18, 309-328 (1982).
[CrossRef]

1981 (1)

K. Ikeuchi, “Determining surface orientations of specular surfaces by using the photometric stereo method,” IEEE Trans. Pattern Anal. Machine Intell. pami-3, 661-669 (1981).
[CrossRef]

1980 (1)

R. J. Woodham, “Photometric method for determining surface orientation from multiple images,” Opt. Eng. 19, 139-144(1980).

Agarwal, S.

M. Chandraker, S. Agarwal, and D. Kriegman, “ShadowCuts: photometric stereo with shadows,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8.

Amano, K.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color Res. Appl. 32, 352-360 (2007).
[CrossRef]

Arjona, M.

M. de Lasarte, J. Pujol, M. Arjona, and M. Vilaseca, “Influence of the size of the training set on color measurements performed using a multispectral imaging system,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 437-440.

Barsky, S.

S. Barsky and M. Petrou, “The 4-source photometric stereo technique for 3-dimensional surfaces in the presence of highlights and shadows,” IEEE Trans. Pattern Anal. Machine Intell. 25, 1239-1252 (2003).
[CrossRef]

S. Barsky and M. Petrou, “Color photometric stereo: Simultaneous reconstruction of local gradient and color of rough textured surfaces,” in Proceedings of Eighth IEEE International Conference on Computer Vision (IEEE, 2001), pp. 600-605.
[CrossRef]

Berns, R.

F. H. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives (Society of Multispectral Imaging of Japan, 1999), pp. 42-49.

Brettel, H.

J. Hardeberg, F. Schmidtt, H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41, 2532-2548 (2002).
[CrossRef]

Bringier, B.

B. Bringier, D. Helbert, and M. Khoudeir, “Photometric reconstruction of a dynamic textured surface from just one color image acquisition,” J. Opt. Soc. Am. 25, 566-574 (2008).
[CrossRef]

Brostow, G. J.

C. Hernández, G. Vogiatzis, G. J. Brostow, B. Stengar, and R. Cipolla, “Non-rigid photometric stereo with colored lights,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.

Burguer, P.

B. Kim and P. Burguer, “Depth and shape from shading using the photometric stereo method,” Comp. Vis. Graph. Image Process. 54, 416-427 (1991).

Chandraker, M.

M. Chandraker, S. Agarwal, and D. Kriegman, “ShadowCuts: photometric stereo with shadows,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8.

Chantler, M.

G. McGunnigle and M. Chantler, “Rough surface description using photometric stereo,” Meas. Sci. Technol. 14, 699-709 (2003).
[CrossRef]

A. Spence and M. Chantler, “On capturing 3D isotropic surface texture using uncalibrated photometric stereo,” in 3rd International Workshop on Texture Analysis and Synthesis (TextureLab, 2003), pp. 83-88.

Chen, C.-F.

C.-Yen Chen, R. Klette, and C.-F. Chen, “Recovery of colored surface reflectances using the photometric stereo method,” in Proceedings of International Conference on Information Systems (Association for Information Systems, 2003), pp. 969-974.

Chen, C.-Yen

C.-Yen Chen, R. Klette, and C.-F. Chen, “Recovery of colored surface reflectances using the photometric stereo method,” in Proceedings of International Conference on Information Systems (Association for Information Systems, 2003), pp. 969-974.

Christensen, P. H.

P. H. Christensen and L. G. Shapiro “Three dimensional shape from color photometric stereo,” Int. J. Comput. Vis. 13, 213-227 (1994).
[CrossRef]

Cipolla, R.

C. Hernández, G. Vogiatzis, and R. Cipolla, “Multiview photometric stereo,” IEEE Trans. Pattern Anal. Machine Intell. 30, 548-554 (2008).
[CrossRef]

C. Hernández, G. Vogiatzis, G. J. Brostow, B. Stengar, and R. Cipolla, “Non-rigid photometric stereo with colored lights,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.

C. Hernández, Vogiatzis, and R. Cipolla, “Shadows in three-source photometric stereo,” in Proceedings of IEEE European Conference on Computer Vision (IEEE, 2008), pp. 1-14.

Coleman, E.

E. Coleman Jr. and R. Jain, “Obtaining 3-Dimensional shape of textured and specular surfaces using four-source photometry,” Comp. Graph. Image Process. 18, 309-328 (1982).
[CrossRef]

Corbalan,

Corbalan, Millan, and Yzuel, “Color measurement in standard CIELab coordinates using a 3CCD camera: correction for the influence of the light source,” Opt. Eng. 39, 1470-1476(2000).
[CrossRef]

de Figuiredo, R.

H. Tagare and R. de Figuiredo, “A theory of photometric stereo for a class of diffuse non-Lambertian surfaces,” IEEE Trans. Pattern Anal. Machine Intell. 13, 133-152 (1991).
[CrossRef]

de Lasarte, M.

M. de Lasarte, J. Pujol, M. Arjona, and M. Vilaseca, “Influence of the size of the training set on color measurements performed using a multispectral imaging system,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 437-440.

Drew, M. S.

M. S. Drew, “Photometric stereo without multiple images,” Proc. SPIE 3016, 369-380 (1997).
[CrossRef]

M. S. Drew, “Optimization approach to dichromatic images,” J. Math. Imaging Vis. 3, 187 (1993).
[CrossRef]

M. S. Drew, “Shape from color,” Technical Report CSS/LCCR TR 92-07 (Simon Fraser University School of Computing Science, 1992).

Ferreira, F. P.

Foster, D. H.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color Res. Appl. 32, 352-360 (2007).
[CrossRef]

S. M. C. Nascimento, F. P. Ferreira, and D. H. Foster, “Statistics of spatial cone excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484-1490 (2002).
[CrossRef]

Hardeberg, J.

J. Hardeberg, F. Schmidtt, H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41, 2532-2548 (2002).
[CrossRef]

Healey, G.

G. Healey and L. Wang, “Three-dimensional surface segmentation using multicolored illumination,” Opt. Eng. 37, 1553-1562 (1998).
[CrossRef]

Helbert, D.

B. Bringier, D. Helbert, and M. Khoudeir, “Photometric reconstruction of a dynamic textured surface from just one color image acquisition,” J. Opt. Soc. Am. 25, 566-574 (2008).
[CrossRef]

Hernández, C.

C. Hernández, G. Vogiatzis, and R. Cipolla, “Multiview photometric stereo,” IEEE Trans. Pattern Anal. Machine Intell. 30, 548-554 (2008).
[CrossRef]

C. Hernández, G. Vogiatzis, G. J. Brostow, B. Stengar, and R. Cipolla, “Non-rigid photometric stereo with colored lights,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.

C. Hernández, Vogiatzis, and R. Cipolla, “Shadows in three-source photometric stereo,” in Proceedings of IEEE European Conference on Computer Vision (IEEE, 2008), pp. 1-14.

Hernández-Andrés, J.

Ikeuchi, K.

K. Ikeuchi, “Determining surface orientations of specular surfaces by using the photometric stereo method,” IEEE Trans. Pattern Anal. Machine Intell. pami-3, 661-669 (1981).
[CrossRef]

Imai, F. H.

F. H. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives (Society of Multispectral Imaging of Japan, 1999), pp. 42-49.

Jain, R.

E. Coleman Jr. and R. Jain, “Obtaining 3-Dimensional shape of textured and specular surfaces using four-source photometry,” Comp. Graph. Image Process. 18, 309-328 (1982).
[CrossRef]

Khoudeir, M.

B. Bringier, D. Helbert, and M. Khoudeir, “Photometric reconstruction of a dynamic textured surface from just one color image acquisition,” J. Opt. Soc. Am. 25, 566-574 (2008).
[CrossRef]

Kim, B.

B. Kim and P. Burguer, “Depth and shape from shading using the photometric stereo method,” Comp. Vis. Graph. Image Process. 54, 416-427 (1991).

Klette, R.

C.-Yen Chen, R. Klette, and C.-F. Chen, “Recovery of colored surface reflectances using the photometric stereo method,” in Proceedings of International Conference on Information Systems (Association for Information Systems, 2003), pp. 969-974.

Kontsevich, L. L.

Kriegman, D.

M. Chandraker, S. Agarwal, and D. Kriegman, “ShadowCuts: photometric stereo with shadows,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8.

McGunnigle, G.

G. McGunnigle and M. Chantler, “Rough surface description using photometric stereo,” Meas. Sci. Technol. 14, 699-709 (2003).
[CrossRef]

Millan,

Corbalan, Millan, and Yzuel, “Color measurement in standard CIELab coordinates using a 3CCD camera: correction for the influence of the light source,” Opt. Eng. 39, 1470-1476(2000).
[CrossRef]

Nascimento, S. M. C.

Nayar, S. K.

M. Oren and S. K. Nayar, “Generalization of Lambert's reflectance model,” Comp. Graph. 28, 239-246 (1994).
[CrossRef]

Nieves, J. L.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color Res. Appl. 32, 352-360 (2007).
[CrossRef]

J. L. Nieves, E. M. Valero, S. M. C. Nascimento, J. Hernández-Andrés, and J. Romero “Multispectral synthesis of daylight using a commercial digital CCD camera,” Appl. Opt. 44, 5696-5703 (2005).
[CrossRef] [PubMed]

C. Plata, E. M. Valero, J. L. Nieves, and J. Romero, “Supervised training sample selection for the estimation of spectral reflectance using a RGB camera,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 519-522.

C. Plata, J. L. Nieves, and J. Romero, “Combining spectral and photometric stereo techniques for reflectance estimation using an RGB digital camera,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 516-518.

Oren, M.

M. Oren and S. K. Nayar, “Generalization of Lambert's reflectance model,” Comp. Graph. 28, 239-246 (1994).
[CrossRef]

Petrou, M.

S. Barsky and M. Petrou, “The 4-source photometric stereo technique for 3-dimensional surfaces in the presence of highlights and shadows,” IEEE Trans. Pattern Anal. Machine Intell. 25, 1239-1252 (2003).
[CrossRef]

S. Barsky and M. Petrou, “Color photometric stereo: Simultaneous reconstruction of local gradient and color of rough textured surfaces,” in Proceedings of Eighth IEEE International Conference on Computer Vision (IEEE, 2001), pp. 600-605.
[CrossRef]

Petrov, A. P.

Plata, C.

C. Plata, E. M. Valero, J. L. Nieves, and J. Romero, “Supervised training sample selection for the estimation of spectral reflectance using a RGB camera,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 519-522.

C. Plata, J. L. Nieves, and J. Romero, “Combining spectral and photometric stereo techniques for reflectance estimation using an RGB digital camera,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 516-518.

Pujol, J.

M. de Lasarte, J. Pujol, M. Arjona, and M. Vilaseca, “Influence of the size of the training set on color measurements performed using a multispectral imaging system,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 437-440.

Romero, J.

J. L. Nieves, E. M. Valero, S. M. C. Nascimento, J. Hernández-Andrés, and J. Romero “Multispectral synthesis of daylight using a commercial digital CCD camera,” Appl. Opt. 44, 5696-5703 (2005).
[CrossRef] [PubMed]

C. Plata, E. M. Valero, J. L. Nieves, and J. Romero, “Supervised training sample selection for the estimation of spectral reflectance using a RGB camera,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 519-522.

C. Plata, J. L. Nieves, and J. Romero, “Combining spectral and photometric stereo techniques for reflectance estimation using an RGB digital camera,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 516-518.

Schl, K.

K. Schl and O. Witting, “Photometric stereo for non-Lambertian surfaces using color information,” in Proceedings of 5th International Conference on Computer Analysis of Images and Patterns (Springer, 1993), pp. 444-451.

Schmidtt, F.

J. Hardeberg, F. Schmidtt, H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41, 2532-2548 (2002).
[CrossRef]

Shapiro, L. G.

P. H. Christensen and L. G. Shapiro “Three dimensional shape from color photometric stereo,” Int. J. Comput. Vis. 13, 213-227 (1994).
[CrossRef]

Shimano, N.

N. Shimano, “Evaluation of a multispectral image acquisition system aimed at reconstruction of spectral reflectances,” Opt. Eng. 44, 107005 (2005).
[CrossRef]

Spence, A.

A. Spence and M. Chantler, “On capturing 3D isotropic surface texture using uncalibrated photometric stereo,” in 3rd International Workshop on Texture Analysis and Synthesis (TextureLab, 2003), pp. 83-88.

Stengar, B.

C. Hernández, G. Vogiatzis, G. J. Brostow, B. Stengar, and R. Cipolla, “Non-rigid photometric stereo with colored lights,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.

Tagare, H.

H. Tagare and R. de Figuiredo, “A theory of photometric stereo for a class of diffuse non-Lambertian surfaces,” IEEE Trans. Pattern Anal. Machine Intell. 13, 133-152 (1991).
[CrossRef]

Tang, K.-L.

21. T.-P. Wu, K.-L. Tang, and T.-T. Wong, “Dense photometric stereo: a Markov random field approach,” IEEE Trans. Pattern Anal. Machine Intell. 28, 1830-1846 (2006).
[CrossRef]

Valero, E. M.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color Res. Appl. 32, 352-360 (2007).
[CrossRef]

J. L. Nieves, E. M. Valero, S. M. C. Nascimento, J. Hernández-Andrés, and J. Romero “Multispectral synthesis of daylight using a commercial digital CCD camera,” Appl. Opt. 44, 5696-5703 (2005).
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M. de Lasarte, J. Pujol, M. Arjona, and M. Vilaseca, “Influence of the size of the training set on color measurements performed using a multispectral imaging system,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 437-440.

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C. Hernández, Vogiatzis, and R. Cipolla, “Shadows in three-source photometric stereo,” in Proceedings of IEEE European Conference on Computer Vision (IEEE, 2008), pp. 1-14.

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E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color Res. Appl. 32, 352-360 (2007).
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C. Hernández, G. Vogiatzis, and R. Cipolla, “Multiview photometric stereo,” IEEE Trans. Pattern Anal. Machine Intell. 30, 548-554 (2008).
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[CrossRef]

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Other (14)

A. Spence and M. Chantler, “On capturing 3D isotropic surface texture using uncalibrated photometric stereo,” in 3rd International Workshop on Texture Analysis and Synthesis (TextureLab, 2003), pp. 83-88.

C. Hernández, G. Vogiatzis, G. J. Brostow, B. Stengar, and R. Cipolla, “Non-rigid photometric stereo with colored lights,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.

M. Chandraker, S. Agarwal, and D. Kriegman, “ShadowCuts: photometric stereo with shadows,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8.

C. Hernández, Vogiatzis, and R. Cipolla, “Shadows in three-source photometric stereo,” in Proceedings of IEEE European Conference on Computer Vision (IEEE, 2008), pp. 1-14.

K. Schl and O. Witting, “Photometric stereo for non-Lambertian surfaces using color information,” in Proceedings of 5th International Conference on Computer Analysis of Images and Patterns (Springer, 1993), pp. 444-451.

M. S. Drew, “Shape from color,” Technical Report CSS/LCCR TR 92-07 (Simon Fraser University School of Computing Science, 1992).

C. Plata, J. L. Nieves, and J. Romero, “Combining spectral and photometric stereo techniques for reflectance estimation using an RGB digital camera,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 516-518.

R. J. Woodham, “Reflectance map techniques for analyzing surface defects in metal castings,” Technical Report AI-TR-457 ( MIT, Artificial Intelligence Laboratory, 1987).

F. H. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives (Society of Multispectral Imaging of Japan, 1999), pp. 42-49.

S. Barsky and M. Petrou, “Color photometric stereo: Simultaneous reconstruction of local gradient and color of rough textured surfaces,” in Proceedings of Eighth IEEE International Conference on Computer Vision (IEEE, 2001), pp. 600-605.
[CrossRef]

C.-Yen Chen, R. Klette, and C.-F. Chen, “Recovery of colored surface reflectances using the photometric stereo method,” in Proceedings of International Conference on Information Systems (Association for Information Systems, 2003), pp. 969-974.

M. de Lasarte, J. Pujol, M. Arjona, and M. Vilaseca, “Influence of the size of the training set on color measurements performed using a multispectral imaging system,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 437-440.

C. Plata, E. M. Valero, J. L. Nieves, and J. Romero, “Supervised training sample selection for the estimation of spectral reflectance using a RGB camera,” in Color in Graphics, Imaging and Vision (CGIV) '08 and Multispectral Colour Science (MCS) '08 Final Program and Proceedings (International Science and Technology, 2008), pp. 519-522.

Munsell Book of Color--Matte Finish Collection (Munsell Color, 1976).

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Figures (11)

Fig. 1
Fig. 1

Definition of the important vectors and reflectance angles: R, viewer vector; L, illuminant vector; N, normal vector; i, angle of incidence; e, angle of emittance; g, phase angle.

Fig. 2
Fig. 2

Example of the distribution of one Munsell hue group’s sample in the RGB space in digital counts as captured by our Retiga camera.

Fig. 3
Fig. 3

Experimental setup. Camera and sample can go around together, maintaining the relative position between them. By fixing the position of a source and making the camera–sample set move, it is possible to obtain any desired direction of illumination.

Fig. 4
Fig. 4

Normalized SPDs of the illumination sources.

Fig. 5
Fig. 5

Example of the two different kinds of textures of the samples used in this work. The first image represents an example of the smooth, terrainlike samples, and the second one is an example of the more abrupt texture.

Fig. 6
Fig. 6

Example of simulated sample not included in the training set. First line, histogram of RGB differences. Second line, original sample and color-scale image showing the distribution of RGB differences in the images.

Fig. 7
Fig. 7

Comparison of reflectance recovered with our algorithm and measured with a spectroradiometer. (a) Original image with the measured areas marked. (b) Reflectances recovered with our algorithm. (c) Reflectances measured with a PR650 spectroradiometer.

Fig. 8
Fig. 8

Spectral sensitivities of the RGB digital camera.

Fig. 9
Fig. 9

Example of simulation made with the albedo calculated using the reflectance obtained by the pseudoinverse method and the SPD of the Digilite lamp, showing the same information as Fig. 6.

Fig. 10
Fig. 10

Example of simulation made with the albedo calculated using the reflectance obtained by the pseudoinverse method and the SPD of the incandescent lamp, showing the same information as Fig. 6.

Fig. 11
Fig. 11

Example of simulation made with the albedo calculated using the reflectance obtained by the pseudoinverse method and the SPD of the Trilite lamp, showing the same information as Fig. 6.

Tables (3)

Tables Icon

Table 1 Pixel-by-Pixel Statistics Obtained When Original and Rendered Images From the Albedo and Normal Recovery Step are Compared

Tables Icon

Table 2 Pixel-by-Pixel Statistics Obtained When Original and Rendered Images From the Spectral Recovery Step are Compared

Tables Icon

Table 3 Overall Statistic Results From the Spectral Recovery Step

Equations (14)

Equations on this page are rendered with MathJax. Learn more.

p ( x , y ) = S ( x , y ) x , q ( x , y ) = S ( x , y ) y ,
N = 1 p 2 + q 2 + 1 ( p , q , 1 ) T ,
I = ρ ( L · N ) ,
I k = ρ ( L k · N ) ,
I = ρ [ L ] N
[ L ] 1 I = ρ N .
I j x = ρ j x [ L ] j N j x ,
[ L ] j 1 I j x = ρ j x N j x ,
N x = 1 3 i N i x ,
D = S q +
S 1 = D q 1
ρ = E ( λ ) S ( λ ) Q ( λ ) ,
RGB error x = 1 3 ( Δ R x 2 + Δ G x 2 + Δ B x 2 ) ,
A E x = cos 1 ( ρ o · ρ e ) ,

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