V. Babaei, S. H. Amirshahi, and F. Agahian, “Using weighted pseudo-inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality,” Color Res. Appl. 36, 295–305 (2011).

[CrossRef]

S. G. Kandi and M. A. Tehran, “Applying metamer sets to investigate data dependency of principal component analysis method in recovery of spectral data,” Color Res. Appl. 36, 349–354 (2011).

[CrossRef]

J. Kim and H. Park, “Fast nonnegative matrix factorization: an active-set-like method and comparisons,” SIAM J. Sci. Comput. 33, 3261–3281 (2011).

[CrossRef]

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

S. Zuffi, S. Santini, and R. Schettini, “From color sensor space to feasible reflectance spectra,” IEEE Trans. Signal Process. 56, 518–531 (2008).

[CrossRef]

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. 43, 1351–1362 (2005).

[CrossRef]

H. S. Fairman and M. H. Brill, “The principal components of reflectance,” Color Res. Appl. 29, 104–110 (2004).

[CrossRef]

I. Amidror, “Scattered data interpolation methods for electronic imaging systems: A survey,” J. Electron. Imaging 11, 157–176 (2002).

[CrossRef]

D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature 401, 788–791 (1999).

[CrossRef]

J. Kasson, W. Plouffe, and S. Nin, “A tetrahedral interpolation technique for color space conversion,” Proc. SPIE 1909, 127–138 (1993).

[CrossRef]

V. Babaei, S. H. Amirshahi, and F. Agahian, “Using weighted pseudo-inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality,” Color Res. Appl. 36, 295–305 (2011).

[CrossRef]

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

I. Amidror, “Scattered data interpolation methods for electronic imaging systems: A survey,” J. Electron. Imaging 11, 157–176 (2002).

[CrossRef]

S. H. Amirshahi and S. A. Amirhahi, “Adaptive non-negative bases for reconstruction of spectral data from colorimetric information,” Opt. Rev. 17, 562–569 (2010).

[CrossRef]

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

V. Babaei, S. H. Amirshahi, and F. Agahian, “Using weighted pseudo-inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality,” Color Res. Appl. 36, 295–305 (2011).

[CrossRef]

S. H. Amirshahi and S. A. Amirhahi, “Adaptive non-negative bases for reconstruction of spectral data from colorimetric information,” Opt. Rev. 17, 562–569 (2010).

[CrossRef]

F. M. Abed, S. H. Amirshahi, and M. R. M. Abed, “Reconstruction of reflectance data using an interpolation technique,” J. Opt. Soc. Am. A 26, 613–624 (2009).

[CrossRef]

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

E. B. Magrab, S. Azarm, B. Balachandran, J. H. Duncan, K. E. Herold, and G. C. Walsh, An Engineer’s Guide to Matlab, 3rd ed. (Pearson, 2011).

V. Babaei, S. H. Amirshahi, and F. Agahian, “Using weighted pseudo-inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality,” Color Res. Appl. 36, 295–305 (2011).

[CrossRef]

E. B. Magrab, S. Azarm, B. Balachandran, J. H. Duncan, K. E. Herold, and G. C. Walsh, An Engineer’s Guide to Matlab, 3rd ed. (Pearson, 2011).

R. S. Berns, Billmeyer and Saltzman’s Principles of Color Technology, 3rd ed. (Wiley, 2000).

H. S. Fairman and M. H. Brill, “The principal components of reflectance,” Color Res. Appl. 29, 104–110 (2004).

[CrossRef]

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. 43, 1351–1362 (2005).

[CrossRef]

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. 43, 1351–1362 (2005).

[CrossRef]

M. de Berg, M. van Krefeld, M. Overmars, and O. Schwarzkopf, Computational Geometry: Algorithms and Applications, 2nd ed. (Springer, 2000).

E. B. Magrab, S. Azarm, B. Balachandran, J. H. Duncan, K. E. Herold, and G. C. Walsh, An Engineer’s Guide to Matlab, 3rd ed. (Pearson, 2011).

H. S. Fairman and M. H. Brill, “The principal components of reflectance,” Color Res. Appl. 29, 104–110 (2004).

[CrossRef]

P. Green and L. MacDonald, Color Engineering Achieving Device Independent Colour (Addison-Wesley, 2002).

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

E. B. Magrab, S. Azarm, B. Balachandran, J. H. Duncan, K. E. Herold, and G. C. Walsh, An Engineer’s Guide to Matlab, 3rd ed. (Pearson, 2011).

S. G. Kandi and M. A. Tehran, “Applying metamer sets to investigate data dependency of principal component analysis method in recovery of spectral data,” Color Res. Appl. 36, 349–354 (2011).

[CrossRef]

J. Kasson, W. Plouffe, and S. Nin, “A tetrahedral interpolation technique for color space conversion,” Proc. SPIE 1909, 127–138 (1993).

[CrossRef]

J. Kim and H. Park, “Fast nonnegative matrix factorization: an active-set-like method and comparisons,” SIAM J. Sci. Comput. 33, 3261–3281 (2011).

[CrossRef]

D. A. Landgrebe, “Information extraction principles and methods for multispectral and hyperspectral remote sensing,” in Information Processing for Remote Sensing, C. H. Chen, ed. (World Scientific, 1999).

D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature 401, 788–791 (1999).

[CrossRef]

D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in Proceedings of Neural Information Processing Systems (MIT, 2000), Vol. 13, pp. 556–562.

L. W. MacDonald and R. Luo, Colour Imaging: Vision and Technology (Wiley, 1999).

P. Green and L. MacDonald, Color Engineering Achieving Device Independent Colour (Addison-Wesley, 2002).

L. W. MacDonald and R. Luo, Colour Imaging: Vision and Technology (Wiley, 1999).

E. B. Magrab, S. Azarm, B. Balachandran, J. H. Duncan, K. E. Herold, and G. C. Walsh, An Engineer’s Guide to Matlab, 3rd ed. (Pearson, 2011).

W. L. Martinez and A. R. Martinez, Computational Statistics Handbook with Matlab, 2nd ed. (Chapman & Hall, 2002).

W. L. Martinez, A. R. Martinez, and J. L. Solka, Exploratory Data Analysis with Matlab, 2nd ed. (Chapman & Hall, 2011).

W. L. Martinez, A. R. Martinez, and J. L. Solka, Exploratory Data Analysis with Matlab, 2nd ed. (Chapman & Hall, 2011).

W. L. Martinez and A. R. Martinez, Computational Statistics Handbook with Matlab, 2nd ed. (Chapman & Hall, 2002).

J. Kasson, W. Plouffe, and S. Nin, “A tetrahedral interpolation technique for color space conversion,” Proc. SPIE 1909, 127–138 (1993).

[CrossRef]

N. Ohta and A. Robertson, Colorimetry: Fundamentals and Applications, 1st ed. (Wiley, 2006).

M. de Berg, M. van Krefeld, M. Overmars, and O. Schwarzkopf, Computational Geometry: Algorithms and Applications, 2nd ed. (Springer, 2000).

J. Kim and H. Park, “Fast nonnegative matrix factorization: an active-set-like method and comparisons,” SIAM J. Sci. Comput. 33, 3261–3281 (2011).

[CrossRef]

J. Kasson, W. Plouffe, and S. Nin, “A tetrahedral interpolation technique for color space conversion,” Proc. SPIE 1909, 127–138 (1993).

[CrossRef]

S. Westland and C. Ripamonti, Computational Color Science Using Matlab (Wiley, 2004).

N. Ohta and A. Robertson, Colorimetry: Fundamentals and Applications, 1st ed. (Wiley, 2006).

S. Zuffi, S. Santini, and R. Schettini, “From color sensor space to feasible reflectance spectra,” IEEE Trans. Signal Process. 56, 518–531 (2008).

[CrossRef]

J. Schanda, Colorimetry: Understanding the CIE System, 1st ed. (Wiley, 2007).

S. Zuffi, S. Santini, and R. Schettini, “From color sensor space to feasible reflectance spectra,” IEEE Trans. Signal Process. 56, 518–531 (2008).

[CrossRef]

M. de Berg, M. van Krefeld, M. Overmars, and O. Schwarzkopf, Computational Geometry: Algorithms and Applications, 2nd ed. (Springer, 2000).

D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature 401, 788–791 (1999).

[CrossRef]

D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in Proceedings of Neural Information Processing Systems (MIT, 2000), Vol. 13, pp. 556–562.

W. L. Martinez, A. R. Martinez, and J. L. Solka, Exploratory Data Analysis with Matlab, 2nd ed. (Chapman & Hall, 2011).

S. G. Kandi and M. A. Tehran, “Applying metamer sets to investigate data dependency of principal component analysis method in recovery of spectral data,” Color Res. Appl. 36, 349–354 (2011).

[CrossRef]

M. de Berg, M. van Krefeld, M. Overmars, and O. Schwarzkopf, Computational Geometry: Algorithms and Applications, 2nd ed. (Springer, 2000).

E. B. Magrab, S. Azarm, B. Balachandran, J. H. Duncan, K. E. Herold, and G. C. Walsh, An Engineer’s Guide to Matlab, 3rd ed. (Pearson, 2011).

S. Westland and C. Ripamonti, Computational Color Science Using Matlab (Wiley, 2004).

S. Zuffi, S. Santini, and R. Schettini, “From color sensor space to feasible reflectance spectra,” IEEE Trans. Signal Process. 56, 518–531 (2008).

[CrossRef]

S. G. Kandi and M. A. Tehran, “Applying metamer sets to investigate data dependency of principal component analysis method in recovery of spectral data,” Color Res. Appl. 36, 349–354 (2011).

[CrossRef]

H. S. Fairman and M. H. Brill, “The principal components of reflectance,” Color Res. Appl. 29, 104–110 (2004).

[CrossRef]

V. Babaei, S. H. Amirshahi, and F. Agahian, “Using weighted pseudo-inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality,” Color Res. Appl. 36, 295–305 (2011).

[CrossRef]

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. 43, 1351–1362 (2005).

[CrossRef]

S. Zuffi, S. Santini, and R. Schettini, “From color sensor space to feasible reflectance spectra,” IEEE Trans. Signal Process. 56, 518–531 (2008).

[CrossRef]

I. Amidror, “Scattered data interpolation methods for electronic imaging systems: A survey,” J. Electron. Imaging 11, 157–176 (2002).

[CrossRef]

D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature 401, 788–791 (1999).

[CrossRef]

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

S. H. Amirshahi and S. A. Amirhahi, “Adaptive non-negative bases for reconstruction of spectral data from colorimetric information,” Opt. Rev. 17, 562–569 (2010).

[CrossRef]

J. Kasson, W. Plouffe, and S. Nin, “A tetrahedral interpolation technique for color space conversion,” Proc. SPIE 1909, 127–138 (1993).

[CrossRef]

J. Kim and H. Park, “Fast nonnegative matrix factorization: an active-set-like method and comparisons,” SIAM J. Sci. Comput. 33, 3261–3281 (2011).

[CrossRef]

D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in Proceedings of Neural Information Processing Systems (MIT, 2000), Vol. 13, pp. 556–562.

S. Farajikhah, F. Madanchi, and S. H. Amirshahi, “Nonlinear principal component analysis for compression of spectral data,” in Proceedings of Conference on Data Mining and Data Warehouses (2011), http://ailab.ijs.si/dunja/SiKDD2011/ .

R. S. Berns, Billmeyer and Saltzman’s Principles of Color Technology, 3rd ed. (Wiley, 2000).

J. Schanda, Colorimetry: Understanding the CIE System, 1st ed. (Wiley, 2007).

N. Ohta and A. Robertson, Colorimetry: Fundamentals and Applications, 1st ed. (Wiley, 2006).

D. A. Landgrebe, “Information extraction principles and methods for multispectral and hyperspectral remote sensing,” in Information Processing for Remote Sensing, C. H. Chen, ed. (World Scientific, 1999).

L. W. MacDonald and R. Luo, Colour Imaging: Vision and Technology (Wiley, 1999).

S. Westland and C. Ripamonti, Computational Color Science Using Matlab (Wiley, 2004).

W. L. Martinez and A. R. Martinez, Computational Statistics Handbook with Matlab, 2nd ed. (Chapman & Hall, 2002).

We have prepared the supplementary materials for the detailed comparison of our technique with that of adaptive PCA and the method of [14]. It can be downloaded from our website: http://home.pusan.ac.kr/~boggikim/hybrid/supforjosa1.docx .

P. Green and L. MacDonald, Color Engineering Achieving Device Independent Colour (Addison-Wesley, 2002).

W. L. Martinez, A. R. Martinez, and J. L. Solka, Exploratory Data Analysis with Matlab, 2nd ed. (Chapman & Hall, 2011).

University of Joensuu Color Group, “Spectral Database,” http://spectral.joensuu.fi .

The Bebel Color Company, “RGB coordinates of the Macbeth Color Checker,” http://www.babelcolor.com/main_level/ColorChecker.htm .

The IRI Color Design, “Hue and Tone 120 system,” http://www.iricolor.com .

Nippon Color and Design Research Institute Inc., “The color chart, marketing, and merchandising color chart,” http://www.ncd-ri.co.jp .

E. B. Magrab, S. Azarm, B. Balachandran, J. H. Duncan, K. E. Herold, and G. C. Walsh, An Engineer’s Guide to Matlab, 3rd ed. (Pearson, 2011).

M. de Berg, M. van Krefeld, M. Overmars, and O. Schwarzkopf, Computational Geometry: Algorithms and Applications, 2nd ed. (Springer, 2000).