Abstract

We propose a method for the capture of high dynamic range (HDR), multispectral (MS), polarimetric (Pol) images of indoor scenes using a liquid crystal tunable filter (LCTF). We have included the adaptive exposure estimation (AEE) method to fully automatize the capturing process. We also propose a pre-processing method which can be applied for the registration of HDR images after they are already built as the result of combining different low dynamic range (LDR) images. This method is applied to ensure a correct alignment of the different polarization HDR images for each spectral band. We have focused our efforts in two main applications: object segmentation and classification into metal and dielectric classes. We have simplified the segmentation using mean shift combined with cluster averaging and region merging techniques. We compare the performance of our segmentation with that of Ncut and Watershed methods. For the classification task, we propose to use information not only in the highlight regions but also in their surrounding area, extracted from the degree of linear polarization (DoLP) maps. We present experimental results which proof that the proposed image processing pipeline outperforms previous techniques developed specifically for MSHDRPol image cubes.

© 2017 Optical Society of America

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References

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2015 (3)

2014 (3)

J. M. Medina, J. A. Díaz, and C. Vignolo, “Fractal dimension of sparkles in automotive metallic coatings by multispectral imaging measurements,” ACS Appl. Matter. Inter. 6, 11439–11447 (2014).
[Crossref]

M. Martínez, E. Valero, J. Hernández-Andrés, J. Romero, and G. Langfelder, “Combining Transverse Field Detectors and Color Filter Arrays to improve multispectral imaging systems,” Appl. Opt. 53, C14–C24 (2014).
[Crossref] [PubMed]

J. Song, E. Valero, and J. L. Nieves, “Segmentation of natural scenes: Clustering in colour space vs. spectral estimation and clustering of spectral data,” Proceedings of AIC Congress 12, 2–8 (2014).

2012 (1)

J. McCann and A. Rizzi, “Camera and visual veiling glare in HDR images,” J. Soc. Inf. Display 15, 721–730 (2012).
[Crossref]

2011 (2)

G. Horvátz, R. Hegedüs, A. Barta, A. Farkas, and S. Âkesson, “Imaging polarimetry of the fogbow: polarization characteristics of white rainbows measured in the high Artic,” Appl. Opt. 50, F64–F71 (2011).
[Crossref]

J. Im, S. Lee, and J. Paik, “Improved elastic registration for removing ghost artifacts in high dynamic imaging,” IEEE T. Consum. Electr. 57, 932–935 (2011).
[Crossref]

2009 (1)

2008 (1)

S. Tominaga and A. Kimachi, “Polarization imaging for material classification,” Opt. Eng. 47(12), 123201 (2008).
[Crossref]

2007 (3)

z J. McCann and A. Rizzi, “Veiling glare: the dynamic range limit of HDR images,” Electr. Img. 6492, 64913–64922 (2007).

E. Talvala, A. Adams, M. Horowitz, and M. Levoy, “Veiling glare in high dynamic range imaging,” ACM T. Graphic 37, 26–37 (2007).

A. O. Akyüz and E. Reinhard, “Noise reduction in high dynamic range imaging,” J. Vis. Commun. Image R. 18(5), 366–376 (2007).
[Crossref]

2006 (2)

A. Ferrero, J. Campos, and A. Pons, “Apparent violation of the radiant exposure reciprocity law in interline CCDs,” Appl. Opt. 45, 3991–3997 (2006).
[Crossref] [PubMed]

H. Haneishi, S. Miyahara, and A. Yoshida, “Image acquisition technique for high dynamic range scenes using a multiband camera,” Color Res. Appl. 31, 294–302 (2006).
[Crossref]

2003 (3)

G. Ward, “Fast, robust image registration for composing high dynamic range photographs from hand-held exposures,” Journal of Graphic Tools 8, 17–30 (2003).
[Crossref]

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vision Comput. 21, 977–1000 (2003).
[Crossref]

M. Robertson, A. Borman, and R. L. Stevenson, “Estimation-theoretic approach to dynamic range enhancement using multiple exposures,” J. Electr. Img. 12, 219–228 (2003).

2002 (2)

D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE T. Pattern Anal. 24, 603–619 (2002).
[Crossref]

S. Pal and P. Mitra, “Multispectral image segmentation using the rough-set-initialized EM algorithm,” IEEE T. Geosci. Remote 40, 2495–2501 (2002).
[Crossref]

2000 (1)

J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE T. Pattern Anal. 22, 888–905 (2000).
[Crossref]

1999 (1)

R. Real, “Tables of significant values of Jaccard’s index of similarity,” Misceltext period centered lania Zoologica 22, 29–40 (1999).

1998 (1)

H. Chen and L. B. Wolff, “Polarization phase-based method for material classification in computer vision,” Int. J. Comput. Vision 28, 73–83 (1998).
[Crossref]

1997 (1)

A. Mehnert and P. Jackway, “An improved seeded region growing algorithm,” Pattern Recogn. Lett. 18(10), 1065–1071 (1997).
[Crossref]

1996 (1)

R. Real and J. M. Vargas, “The probabilistic basis of Jaccard’s index of similarity,” Syst. Biol. 45, 380–385 (1996).
[Crossref]

1995 (1)

Y. Cheng, “Mean shift, mode seeking, and clustering,” IEEE T. Pattern Anal. 17, 790–799 (1995).
[Crossref]

1994 (1)

S. Tominaga, “Dichromatic reflection models for a variety of materials,” Color Res. Appl. 19, 277–285 (1994).
[Crossref]

1992 (1)

S. Beucher and F. Meyer, “The morphological approach to segmentation: the Watershed transformation,” Opt. Eng. 34, 433–481 (1992).

1990 (1)

L. B. Wolff, “Polarization-based material classification from specular reflection,” IEEE T. Pattern Anal. 12, 1059–1071 (1990).
[Crossref]

1989 (3)

G. Healey, “Using color for geometry-insensitive segmentation,” J. Opt. Soc. Am. A 6, 920–937 (1989).
[Crossref]

S. Wu, “Design of a liquid crystal based tunable electrooptic filter,” Appl. Opt. 28, 48–52 (1989).
[Crossref] [PubMed]

L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula,” Comm. Com. Inf. Sc. 25, 315–318 (1989).

Adams, A.

E. Talvala, A. Adams, M. Horowitz, and M. Levoy, “Veiling glare in high dynamic range imaging,” ACM T. Graphic 37, 26–37 (2007).

Ajdin, B.

M. Granados, B. Ajdin, M. Wand, C. Theobalt, H. Seidel, and H. Lensch, “Optimal HDR reconstruction with linear digital cameras,” Proc. CVPR IEEE (IEEE, 2010) pp. 215–222.

Âkesson, S.

Akyüz, A. O.

A. O. Akyüz and E. Reinhard, “Noise reduction in high dynamic range imaging,” J. Vis. Commun. Image R. 18(5), 366–376 (2007).
[Crossref]

Arnaud, D.

D. Arnaud, High Dynamic Range imaging: Sensors and Architectures (SPIE, 2012).

Barta, A.

Beucher, S.

S. Beucher and F. Meyer, “The morphological approach to segmentation: the Watershed transformation,” Opt. Eng. 34, 433–481 (1992).

Bishop, G.

W. Hubbard, G. Bishop, T. Gowen, D. Hayter, and G. Innes, “Multispectral-polarimetric sensing for detection of difficult targets,” Proc. SPIE Europe Security and Defence, 71130L (2008).

Blumthaler, M.

Borman, A.

M. Robertson, A. Borman, and R. L. Stevenson, “Estimation-theoretic approach to dynamic range enhancement using multiple exposures,” J. Electr. Img. 12, 219–228 (2003).

Callet, P.

P. Porral, P. Callet, P. Fuchs, T. Muller, and E. Sandré-Chardonnal, “High Dynamic, Spectral, and Polarized Natural Light Environment Acquisition,” Proceedings of SPIE/IS&T Electronic Imaging, 94030B (2015).

Campos, J.

Cao, J.

J. Cao, P. Wang, Y. Dong, and Q. Xu, “A multi-scale texture segmentation method,” Proceedings of 10th IEEE International Conference on Natural Computation (IEEE, 2014), pp. 873–877.

Chandrasekhar, S.

S. Chandrasekhar, Radiative Transfer (Dover Publications, 1960).

Chen, H.

H. Chen and L. B. Wolff, “Polarization phase-based method for material classification in computer vision,” Int. J. Comput. Vision 28, 73–83 (1998).
[Crossref]

Cheng, Y.

Y. Cheng, “Mean shift, mode seeking, and clustering,” IEEE T. Pattern Anal. 17, 790–799 (1995).
[Crossref]

Comaniciu, D.

D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE T. Pattern Anal. 24, 603–619 (2002).
[Crossref]

Davis, J.

O. Wang, P. Gunawardane, S. Scher, and J. Davis, “Material classification using BRDF slices,” in Proc. CVPR IEEE (IEEE, 2009) pp. 2805–2811.

Debevec, P.

E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward, and K. Myszkowski, High Dynamic Range Imaging: Acquisition, Display, and Image-based Lightning (Morgan Kaufmann, 2010).

P. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in Proceedings of ACM SIGGRAPH pp. 31–40, (2008).

Díaz, J. A.

J. M. Medina, J. A. Díaz, and C. Vignolo, “Fractal dimension of sparkles in automotive metallic coatings by multispectral imaging measurements,” ACS Appl. Matter. Inter. 6, 11439–11447 (2014).
[Crossref]

Dong, Y.

J. Cao, P. Wang, Y. Dong, and Q. Xu, “A multi-scale texture segmentation method,” Proceedings of 10th IEEE International Conference on Natural Computation (IEEE, 2014), pp. 873–877.

Eklung, J.O.

E. Hayman, B. Kaputo, M. Fritz, and J.O. Eklung, “On the significance of real-world conditions for material classification,” in Proceedings of European conference on Computer Vision, (2004) pp. 253–266

Farkas, A.

Ferrero, A.

Flusser, J.

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vision Comput. 21, 977–1000 (2003).
[Crossref]

Fritz, M.

E. Hayman, B. Kaputo, M. Fritz, and J.O. Eklung, “On the significance of real-world conditions for material classification,” in Proceedings of European conference on Computer Vision, (2004) pp. 253–266

Fuchs, P.

P. Porral, P. Callet, P. Fuchs, T. Muller, and E. Sandré-Chardonnal, “High Dynamic, Spectral, and Polarized Natural Light Environment Acquisition,” Proceedings of SPIE/IS&T Electronic Imaging, 94030B (2015).

Gisler, G.

J. Theiler and G. Gisler, “Contiguity-enhanced k-means clustering algorithms for unsupervised multispectral image segmentation,” Proc. SPIE Optical Science, Engineering and Instrumentation, 108–118 (1997).

Goshtasby, A. A.

A. A. Goshtasby, Image Registration: Principles, Tools and Methods (Springer Science & Bussiness Media, 2012).
[Crossref]

Gowen, T.

W. Hubbard, G. Bishop, T. Gowen, D. Hayter, and G. Innes, “Multispectral-polarimetric sensing for detection of difficult targets,” Proc. SPIE Europe Security and Defence, 71130L (2008).

Granados, M.

M. Granados, B. Ajdin, M. Wand, C. Theobalt, H. Seidel, and H. Lensch, “Optimal HDR reconstruction with linear digital cameras,” Proc. CVPR IEEE (IEEE, 2010) pp. 215–222.

Gunawardane, P.

O. Wang, P. Gunawardane, S. Scher, and J. Davis, “Material classification using BRDF slices,” in Proc. CVPR IEEE (IEEE, 2009) pp. 2805–2811.

Hamers, L.

L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula,” Comm. Com. Inf. Sc. 25, 315–318 (1989).

Haneishi, H.

H. Haneishi, S. Miyahara, and A. Yoshida, “Image acquisition technique for high dynamic range scenes using a multiband camera,” Color Res. Appl. 31, 294–302 (2006).
[Crossref]

Hayman, E.

E. Hayman, B. Kaputo, M. Fritz, and J.O. Eklung, “On the significance of real-world conditions for material classification,” in Proceedings of European conference on Computer Vision, (2004) pp. 253–266

Hayter, D.

W. Hubbard, G. Bishop, T. Gowen, D. Hayter, and G. Innes, “Multispectral-polarimetric sensing for detection of difficult targets,” Proc. SPIE Europe Security and Defence, 71130L (2008).

Healey, G.

Hegedüs, R.

Heidrich, W.

E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward, and K. Myszkowski, High Dynamic Range Imaging: Acquisition, Display, and Image-based Lightning (Morgan Kaufmann, 2010).

Hemeryck, Y.

L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula,” Comm. Com. Inf. Sc. 25, 315–318 (1989).

Hernández-Andrés, J.

Herweyers, G.

L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula,” Comm. Com. Inf. Sc. 25, 315–318 (1989).

Hirai, K.

S. Tominaga, H. Kadoi, K. Hirai, and T. Horiuchi, “Metal-dielectric object classification by combining polarization property and surface spectral reflectance,” Proceedings of SPIE/IS&T Electronic Imaging, 86520E (2013).

Horiuchi, T.

S. Tominaga, H. Kadoi, K. Hirai, and T. Horiuchi, “Metal-dielectric object classification by combining polarization property and surface spectral reflectance,” Proceedings of SPIE/IS&T Electronic Imaging, 86520E (2013).

Horowitz, M.

E. Talvala, A. Adams, M. Horowitz, and M. Levoy, “Veiling glare in high dynamic range imaging,” ACM T. Graphic 37, 26–37 (2007).

Horstmeyer, R.

R. Horstmeyer, Multispectral Image Segmentation (MIT Media Lab, 2010).

Horvátz, G.

Hubbard, W.

W. Hubbard, G. Bishop, T. Gowen, D. Hayter, and G. Innes, “Multispectral-polarimetric sensing for detection of difficult targets,” Proc. SPIE Europe Security and Defence, 71130L (2008).

Im, J.

J. Im, S. Lee, and J. Paik, “Improved elastic registration for removing ghost artifacts in high dynamic imaging,” IEEE T. Consum. Electr. 57, 932–935 (2011).
[Crossref]

Innes, G.

W. Hubbard, G. Bishop, T. Gowen, D. Hayter, and G. Innes, “Multispectral-polarimetric sensing for detection of difficult targets,” Proc. SPIE Europe Security and Defence, 71130L (2008).

Jackway, P.

A. Mehnert and P. Jackway, “An improved seeded region growing algorithm,” Pattern Recogn. Lett. 18(10), 1065–1071 (1997).
[Crossref]

Janssen, M.

L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula,” Comm. Com. Inf. Sc. 25, 315–318 (1989).

Kadoi, H.

S. Tominaga, H. Kadoi, K. Hirai, and T. Horiuchi, “Metal-dielectric object classification by combining polarization property and surface spectral reflectance,” Proceedings of SPIE/IS&T Electronic Imaging, 86520E (2013).

Kanade, T.

Y. Tsin, V. Ramesh, and T. Kanade, “Statistical calibration of CCD imaging process,” in Proceedings of Eighth International Conference on Computer Vision (ICCV) 1, pp. 480–487 (2001).

Kaputo, B.

E. Hayman, B. Kaputo, M. Fritz, and J.O. Eklung, “On the significance of real-world conditions for material classification,” in Proceedings of European conference on Computer Vision, (2004) pp. 253–266

Keters, H.

L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula,” Comm. Com. Inf. Sc. 25, 315–318 (1989).

Kimachi, A.

S. Tominaga and A. Kimachi, “Polarization imaging for material classification,” Opt. Eng. 47(12), 123201 (2008).
[Crossref]

Können, G. P.

Kreuter, A.

Langfelder, G.

Lee, S.

J. Im, S. Lee, and J. Paik, “Improved elastic registration for removing ghost artifacts in high dynamic imaging,” IEEE T. Consum. Electr. 57, 932–935 (2011).
[Crossref]

Lensch, H.

M. Granados, B. Ajdin, M. Wand, C. Theobalt, H. Seidel, and H. Lensch, “Optimal HDR reconstruction with linear digital cameras,” Proc. CVPR IEEE (IEEE, 2010) pp. 215–222.

Levoy, M.

E. Talvala, A. Adams, M. Horowitz, and M. Levoy, “Veiling glare in high dynamic range imaging,” ACM T. Graphic 37, 26–37 (2007).

MacAdam, D. L.

D. L. MacAdam, “Color matching functions,” in Color Measurement (Springer, 1981), pp. 178–199.
[Crossref]

Malik, J.

J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE T. Pattern Anal. 22, 888–905 (2000).
[Crossref]

P. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in Proceedings of ACM SIGGRAPH pp. 31–40, (2008).

Mann, S.

S. Mann and R. Picard, “Being undigital with digital cameras,” MIT Media Lab Perceptual (1994).

Mantiuk, R.

A. Tomaszewska and R. Mantiuk, “Image registration for multi-exposure high dynamic range image acquisition,” Proceedings of International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), pp. 49–56, (2007).

Martínez, M.

McCann, J.

J. McCann and A. Rizzi, “Camera and visual veiling glare in HDR images,” J. Soc. Inf. Display 15, 721–730 (2012).
[Crossref]

McCann, J. J.

J. J. McCann and A. Rizzi, The Art and Science of HDR Imaging (John Wiley & Sons, 2011).
[Crossref]

McCann, z J.

z J. McCann and A. Rizzi, “Veiling glare: the dynamic range limit of HDR images,” Electr. Img. 6492, 64913–64922 (2007).

Medeiros, R. S.

R. S. Medeiros, J. Scharcanski, and A. Wong, “Natural scene segmentation based on a stochastic texture region merging approach,” Int. Conf. Acoust. Spee. (IEEE, 2013) pp. 1464–1467.

Medina, J. M.

J. M. Medina, J. A. Díaz, and C. Vignolo, “Fractal dimension of sparkles in automotive metallic coatings by multispectral imaging measurements,” ACS Appl. Matter. Inter. 6, 11439–11447 (2014).
[Crossref]

Meer, P.

D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE T. Pattern Anal. 24, 603–619 (2002).
[Crossref]

Mehnert, A.

A. Mehnert and P. Jackway, “An improved seeded region growing algorithm,” Pattern Recogn. Lett. 18(10), 1065–1071 (1997).
[Crossref]

Meyer, F.

S. Beucher and F. Meyer, “The morphological approach to segmentation: the Watershed transformation,” Opt. Eng. 34, 433–481 (1992).

Mitra, P.

S. Pal and P. Mitra, “Multispectral image segmentation using the rough-set-initialized EM algorithm,” IEEE T. Geosci. Remote 40, 2495–2501 (2002).
[Crossref]

Mitsunaga, T.

T. Mitsunaga and S. K. Nayar, “Radiometric self-calibration,” Proc. CVPR IEEE1, (IEEE, 1999) pp. 374–380.

Miyahara, S.

H. Haneishi, S. Miyahara, and A. Yoshida, “Image acquisition technique for high dynamic range scenes using a multiband camera,” Color Res. Appl. 31, 294–302 (2006).
[Crossref]

Muller, T.

P. Porral, P. Callet, P. Fuchs, T. Muller, and E. Sandré-Chardonnal, “High Dynamic, Spectral, and Polarized Natural Light Environment Acquisition,” Proceedings of SPIE/IS&T Electronic Imaging, 94030B (2015).

Myszkowski, K.

E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward, and K. Myszkowski, High Dynamic Range Imaging: Acquisition, Display, and Image-based Lightning (Morgan Kaufmann, 2010).

Nayar, S. K.

Y. Schechner and S. K. Nayar, “Polarization mosaicing: high dynamic range and polarization imaging in a wide field of view,” Proc. SPIE 48th Annual Meeting of Optical Science and Technology, 93–102 (2003).

T. Mitsunaga and S. K. Nayar, “Radiometric self-calibration,” Proc. CVPR IEEE1, (IEEE, 1999) pp. 374–380.

Nieves, J. L.

J. Song, E. Valero, and J. L. Nieves, “Segmentation of natural scenes: Clustering in colour space vs. spectral estimation and clustering of spectral data,” Proceedings of AIC Congress 12, 2–8 (2014).

Paik, J.

J. Im, S. Lee, and J. Paik, “Improved elastic registration for removing ghost artifacts in high dynamic imaging,” IEEE T. Consum. Electr. 57, 932–935 (2011).
[Crossref]

Pal, S.

S. Pal and P. Mitra, “Multispectral image segmentation using the rough-set-initialized EM algorithm,” IEEE T. Geosci. Remote 40, 2495–2501 (2002).
[Crossref]

Pattanaik, S.

E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward, and K. Myszkowski, High Dynamic Range Imaging: Acquisition, Display, and Image-based Lightning (Morgan Kaufmann, 2010).

Picard, R.

S. Mann and R. Picard, “Being undigital with digital cameras,” MIT Media Lab Perceptual (1994).

Pons, A.

Porral, P.

P. Porral, P. Callet, P. Fuchs, T. Muller, and E. Sandré-Chardonnal, “High Dynamic, Spectral, and Polarized Natural Light Environment Acquisition,” Proceedings of SPIE/IS&T Electronic Imaging, 94030B (2015).

Ramesh, V.

Y. Tsin, V. Ramesh, and T. Kanade, “Statistical calibration of CCD imaging process,” in Proceedings of Eighth International Conference on Computer Vision (ICCV) 1, pp. 480–487 (2001).

Real, R.

R. Real, “Tables of significant values of Jaccard’s index of similarity,” Misceltext period centered lania Zoologica 22, 29–40 (1999).

R. Real and J. M. Vargas, “The probabilistic basis of Jaccard’s index of similarity,” Syst. Biol. 45, 380–385 (1996).
[Crossref]

Reinhard, E.

A. O. Akyüz and E. Reinhard, “Noise reduction in high dynamic range imaging,” J. Vis. Commun. Image R. 18(5), 366–376 (2007).
[Crossref]

E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward, and K. Myszkowski, High Dynamic Range Imaging: Acquisition, Display, and Image-based Lightning (Morgan Kaufmann, 2010).

Rizzi, A.

J. McCann and A. Rizzi, “Camera and visual veiling glare in HDR images,” J. Soc. Inf. Display 15, 721–730 (2012).
[Crossref]

z J. McCann and A. Rizzi, “Veiling glare: the dynamic range limit of HDR images,” Electr. Img. 6492, 64913–64922 (2007).

J. J. McCann and A. Rizzi, The Art and Science of HDR Imaging (John Wiley & Sons, 2011).
[Crossref]

Robertson, M.

M. Robertson, A. Borman, and R. L. Stevenson, “Estimation-theoretic approach to dynamic range enhancement using multiple exposures,” J. Electr. Img. 12, 219–228 (2003).

Romero, J.

M. Martínez, E. Valero, J. Hernández-Andrés, and J. Romero, “HDR imaging - Automatic Exposure Time Estimation: A novel approach,” Proceedings AIC conference in Tokyo 54(4), pp. 603–608 (2015).

M. Martínez, E. Valero, J. Hernández-Andrés, J. Romero, and G. Langfelder, “Combining Transverse Field Detectors and Color Filter Arrays to improve multispectral imaging systems,” Appl. Opt. 53, C14–C24 (2014).
[Crossref] [PubMed]

Rousseau, R.

L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula,” Comm. Com. Inf. Sc. 25, 315–318 (1989).

Sandré-Chardonnal, E.

P. Porral, P. Callet, P. Fuchs, T. Muller, and E. Sandré-Chardonnal, “High Dynamic, Spectral, and Polarized Natural Light Environment Acquisition,” Proceedings of SPIE/IS&T Electronic Imaging, 94030B (2015).

Scharcanski, J.

R. S. Medeiros, J. Scharcanski, and A. Wong, “Natural scene segmentation based on a stochastic texture region merging approach,” Int. Conf. Acoust. Spee. (IEEE, 2013) pp. 1464–1467.

Schechner, Y.

Y. Schechner and S. K. Nayar, “Polarization mosaicing: high dynamic range and polarization imaging in a wide field of view,” Proc. SPIE 48th Annual Meeting of Optical Science and Technology, 93–102 (2003).

Scher, S.

O. Wang, P. Gunawardane, S. Scher, and J. Davis, “Material classification using BRDF slices,” in Proc. CVPR IEEE (IEEE, 2009) pp. 2805–2811.

Schwarzmann, M.

Seidel, H.

M. Granados, B. Ajdin, M. Wand, C. Theobalt, H. Seidel, and H. Lensch, “Optimal HDR reconstruction with linear digital cameras,” Proc. CVPR IEEE (IEEE, 2010) pp. 215–222.

Shi, J.

J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE T. Pattern Anal. 22, 888–905 (2000).
[Crossref]

Soille, P.

P. Soille, Morphological Image Analysis: Principles and Applications (Springer Science & Business Media, 2013).

Song, J.

J. Song, E. Valero, and J. L. Nieves, “Segmentation of natural scenes: Clustering in colour space vs. spectral estimation and clustering of spectral data,” Proceedings of AIC Congress 12, 2–8 (2014).

Stevenson, R. L.

M. Robertson, A. Borman, and R. L. Stevenson, “Estimation-theoretic approach to dynamic range enhancement using multiple exposures,” J. Electr. Img. 12, 219–228 (2003).

Talvala, E.

E. Talvala, A. Adams, M. Horowitz, and M. Levoy, “Veiling glare in high dynamic range imaging,” ACM T. Graphic 37, 26–37 (2007).

Theiler, J.

J. Theiler and G. Gisler, “Contiguity-enhanced k-means clustering algorithms for unsupervised multispectral image segmentation,” Proc. SPIE Optical Science, Engineering and Instrumentation, 108–118 (1997).

Theobalt, C.

M. Granados, B. Ajdin, M. Wand, C. Theobalt, H. Seidel, and H. Lensch, “Optimal HDR reconstruction with linear digital cameras,” Proc. CVPR IEEE (IEEE, 2010) pp. 215–222.

Tomaszewska, A.

A. Tomaszewska and R. Mantiuk, “Image registration for multi-exposure high dynamic range image acquisition,” Proceedings of International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), pp. 49–56, (2007).

Tominaga, S.

S. Tominaga and A. Kimachi, “Polarization imaging for material classification,” Opt. Eng. 47(12), 123201 (2008).
[Crossref]

S. Tominaga, “Dichromatic reflection models for a variety of materials,” Color Res. Appl. 19, 277–285 (1994).
[Crossref]

S. Tominaga, H. Kadoi, K. Hirai, and T. Horiuchi, “Metal-dielectric object classification by combining polarization property and surface spectral reflectance,” Proceedings of SPIE/IS&T Electronic Imaging, 86520E (2013).

Tsin, Y.

Y. Tsin, V. Ramesh, and T. Kanade, “Statistical calibration of CCD imaging process,” in Proceedings of Eighth International Conference on Computer Vision (ICCV) 1, pp. 480–487 (2001).

Valero, E.

M. Martínez, E. Valero, J. Hernández-Andrés, and J. Romero, “HDR imaging - Automatic Exposure Time Estimation: A novel approach,” Proceedings AIC conference in Tokyo 54(4), pp. 603–608 (2015).

M. Martínez, E. Valero, and J. Hernández-Andrés, “Adaptive exposure estimation for high dynamic range imaging applied to natural scenes and daylight skies,” Appl. Opt. 54(4), B241–B250 (2015).
[Crossref] [PubMed]

J. Song, E. Valero, and J. L. Nieves, “Segmentation of natural scenes: Clustering in colour space vs. spectral estimation and clustering of spectral data,” Proceedings of AIC Congress 12, 2–8 (2014).

M. Martínez, E. Valero, J. Hernández-Andrés, J. Romero, and G. Langfelder, “Combining Transverse Field Detectors and Color Filter Arrays to improve multispectral imaging systems,” Appl. Opt. 53, C14–C24 (2014).
[Crossref] [PubMed]

Vanhoutte, A.

L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula,” Comm. Com. Inf. Sc. 25, 315–318 (1989).

Vargas, J. M.

R. Real and J. M. Vargas, “The probabilistic basis of Jaccard’s index of similarity,” Syst. Biol. 45, 380–385 (1996).
[Crossref]

Varma, M.

M. Varma and A. Zisserman, “Classifying images of materials: Achieving viewpoint and illumination independence,” Proceedings of European Conference on Computer Vision (2002) pp. 255–271.

Vignolo, C.

J. M. Medina, J. A. Díaz, and C. Vignolo, “Fractal dimension of sparkles in automotive metallic coatings by multispectral imaging measurements,” ACS Appl. Matter. Inter. 6, 11439–11447 (2014).
[Crossref]

Wand, M.

M. Granados, B. Ajdin, M. Wand, C. Theobalt, H. Seidel, and H. Lensch, “Optimal HDR reconstruction with linear digital cameras,” Proc. CVPR IEEE (IEEE, 2010) pp. 215–222.

Wang, O.

O. Wang, P. Gunawardane, S. Scher, and J. Davis, “Material classification using BRDF slices,” in Proc. CVPR IEEE (IEEE, 2009) pp. 2805–2811.

Wang, P.

J. Cao, P. Wang, Y. Dong, and Q. Xu, “A multi-scale texture segmentation method,” Proceedings of 10th IEEE International Conference on Natural Computation (IEEE, 2014), pp. 873–877.

Ward, G.

G. Ward, “Fast, robust image registration for composing high dynamic range photographs from hand-held exposures,” Journal of Graphic Tools 8, 17–30 (2003).
[Crossref]

E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward, and K. Myszkowski, High Dynamic Range Imaging: Acquisition, Display, and Image-based Lightning (Morgan Kaufmann, 2010).

Wolff, L. B.

H. Chen and L. B. Wolff, “Polarization phase-based method for material classification in computer vision,” Int. J. Comput. Vision 28, 73–83 (1998).
[Crossref]

L. B. Wolff, “Polarization-based material classification from specular reflection,” IEEE T. Pattern Anal. 12, 1059–1071 (1990).
[Crossref]

Wong, A.

R. S. Medeiros, J. Scharcanski, and A. Wong, “Natural scene segmentation based on a stochastic texture region merging approach,” Int. Conf. Acoust. Spee. (IEEE, 2013) pp. 1464–1467.

Wu, S.

Xu, Q.

J. Cao, P. Wang, Y. Dong, and Q. Xu, “A multi-scale texture segmentation method,” Proceedings of 10th IEEE International Conference on Natural Computation (IEEE, 2014), pp. 873–877.

Yoshida, A.

H. Haneishi, S. Miyahara, and A. Yoshida, “Image acquisition technique for high dynamic range scenes using a multiband camera,” Color Res. Appl. 31, 294–302 (2006).
[Crossref]

Zangerl, M.

Zisserman, A.

M. Varma and A. Zisserman, “Classifying images of materials: Achieving viewpoint and illumination independence,” Proceedings of European Conference on Computer Vision (2002) pp. 255–271.

Zitova, B.

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vision Comput. 21, 977–1000 (2003).
[Crossref]

ACM T. Graphic (1)

E. Talvala, A. Adams, M. Horowitz, and M. Levoy, “Veiling glare in high dynamic range imaging,” ACM T. Graphic 37, 26–37 (2007).

ACS Appl. Matter. Inter. (1)

J. M. Medina, J. A. Díaz, and C. Vignolo, “Fractal dimension of sparkles in automotive metallic coatings by multispectral imaging measurements,” ACS Appl. Matter. Inter. 6, 11439–11447 (2014).
[Crossref]

Appl. Opt. (7)

Color Res. Appl. (2)

S. Tominaga, “Dichromatic reflection models for a variety of materials,” Color Res. Appl. 19, 277–285 (1994).
[Crossref]

H. Haneishi, S. Miyahara, and A. Yoshida, “Image acquisition technique for high dynamic range scenes using a multiband camera,” Color Res. Appl. 31, 294–302 (2006).
[Crossref]

Comm. Com. Inf. Sc. (1)

L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula,” Comm. Com. Inf. Sc. 25, 315–318 (1989).

Electr. Img. (1)

z J. McCann and A. Rizzi, “Veiling glare: the dynamic range limit of HDR images,” Electr. Img. 6492, 64913–64922 (2007).

IEEE T. Consum. Electr. (1)

J. Im, S. Lee, and J. Paik, “Improved elastic registration for removing ghost artifacts in high dynamic imaging,” IEEE T. Consum. Electr. 57, 932–935 (2011).
[Crossref]

IEEE T. Geosci. Remote (1)

S. Pal and P. Mitra, “Multispectral image segmentation using the rough-set-initialized EM algorithm,” IEEE T. Geosci. Remote 40, 2495–2501 (2002).
[Crossref]

IEEE T. Pattern Anal. (4)

J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE T. Pattern Anal. 22, 888–905 (2000).
[Crossref]

L. B. Wolff, “Polarization-based material classification from specular reflection,” IEEE T. Pattern Anal. 12, 1059–1071 (1990).
[Crossref]

Y. Cheng, “Mean shift, mode seeking, and clustering,” IEEE T. Pattern Anal. 17, 790–799 (1995).
[Crossref]

D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE T. Pattern Anal. 24, 603–619 (2002).
[Crossref]

Image Vision Comput. (1)

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vision Comput. 21, 977–1000 (2003).
[Crossref]

Int. J. Comput. Vision (1)

H. Chen and L. B. Wolff, “Polarization phase-based method for material classification in computer vision,” Int. J. Comput. Vision 28, 73–83 (1998).
[Crossref]

J. Electr. Img. (1)

M. Robertson, A. Borman, and R. L. Stevenson, “Estimation-theoretic approach to dynamic range enhancement using multiple exposures,” J. Electr. Img. 12, 219–228 (2003).

J. Opt. Soc. Am. A (1)

J. Soc. Inf. Display (1)

J. McCann and A. Rizzi, “Camera and visual veiling glare in HDR images,” J. Soc. Inf. Display 15, 721–730 (2012).
[Crossref]

J. Vis. Commun. Image R. (1)

A. O. Akyüz and E. Reinhard, “Noise reduction in high dynamic range imaging,” J. Vis. Commun. Image R. 18(5), 366–376 (2007).
[Crossref]

Journal of Graphic Tools (1)

G. Ward, “Fast, robust image registration for composing high dynamic range photographs from hand-held exposures,” Journal of Graphic Tools 8, 17–30 (2003).
[Crossref]

Misceltext period centered lania Zoologica (1)

R. Real, “Tables of significant values of Jaccard’s index of similarity,” Misceltext period centered lania Zoologica 22, 29–40 (1999).

Opt. Eng. (2)

S. Tominaga and A. Kimachi, “Polarization imaging for material classification,” Opt. Eng. 47(12), 123201 (2008).
[Crossref]

S. Beucher and F. Meyer, “The morphological approach to segmentation: the Watershed transformation,” Opt. Eng. 34, 433–481 (1992).

Pattern Recogn. Lett. (1)

A. Mehnert and P. Jackway, “An improved seeded region growing algorithm,” Pattern Recogn. Lett. 18(10), 1065–1071 (1997).
[Crossref]

Proceedings AIC conference in Tokyo (1)

M. Martínez, E. Valero, J. Hernández-Andrés, and J. Romero, “HDR imaging - Automatic Exposure Time Estimation: A novel approach,” Proceedings AIC conference in Tokyo 54(4), pp. 603–608 (2015).

Proceedings of AIC Congress (1)

J. Song, E. Valero, and J. L. Nieves, “Segmentation of natural scenes: Clustering in colour space vs. spectral estimation and clustering of spectral data,” Proceedings of AIC Congress 12, 2–8 (2014).

Syst. Biol. (1)

R. Real and J. M. Vargas, “The probabilistic basis of Jaccard’s index of similarity,” Syst. Biol. 45, 380–385 (1996).
[Crossref]

Other (24)

J. Cao, P. Wang, Y. Dong, and Q. Xu, “A multi-scale texture segmentation method,” Proceedings of 10th IEEE International Conference on Natural Computation (IEEE, 2014), pp. 873–877.

R. S. Medeiros, J. Scharcanski, and A. Wong, “Natural scene segmentation based on a stochastic texture region merging approach,” Int. Conf. Acoust. Spee. (IEEE, 2013) pp. 1464–1467.

S. Chandrasekhar, Radiative Transfer (Dover Publications, 1960).

A. A. Goshtasby, Image Registration: Principles, Tools and Methods (Springer Science & Bussiness Media, 2012).
[Crossref]

T. Mitsunaga and S. K. Nayar, “Radiometric self-calibration,” Proc. CVPR IEEE1, (IEEE, 1999) pp. 374–380.

M. Granados, B. Ajdin, M. Wand, C. Theobalt, H. Seidel, and H. Lensch, “Optimal HDR reconstruction with linear digital cameras,” Proc. CVPR IEEE (IEEE, 2010) pp. 215–222.

D. L. MacAdam, “Color matching functions,” in Color Measurement (Springer, 1981), pp. 178–199.
[Crossref]

P. Soille, Morphological Image Analysis: Principles and Applications (Springer Science & Business Media, 2013).

A. Tomaszewska and R. Mantiuk, “Image registration for multi-exposure high dynamic range image acquisition,” Proceedings of International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), pp. 49–56, (2007).

S. Tominaga, H. Kadoi, K. Hirai, and T. Horiuchi, “Metal-dielectric object classification by combining polarization property and surface spectral reflectance,” Proceedings of SPIE/IS&T Electronic Imaging, 86520E (2013).

M. Varma and A. Zisserman, “Classifying images of materials: Achieving viewpoint and illumination independence,” Proceedings of European Conference on Computer Vision (2002) pp. 255–271.

E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward, and K. Myszkowski, High Dynamic Range Imaging: Acquisition, Display, and Image-based Lightning (Morgan Kaufmann, 2010).

J. J. McCann and A. Rizzi, The Art and Science of HDR Imaging (John Wiley & Sons, 2011).
[Crossref]

E. Hayman, B. Kaputo, M. Fritz, and J.O. Eklung, “On the significance of real-world conditions for material classification,” in Proceedings of European conference on Computer Vision, (2004) pp. 253–266

O. Wang, P. Gunawardane, S. Scher, and J. Davis, “Material classification using BRDF slices,” in Proc. CVPR IEEE (IEEE, 2009) pp. 2805–2811.

R. Horstmeyer, Multispectral Image Segmentation (MIT Media Lab, 2010).

J. Theiler and G. Gisler, “Contiguity-enhanced k-means clustering algorithms for unsupervised multispectral image segmentation,” Proc. SPIE Optical Science, Engineering and Instrumentation, 108–118 (1997).

W. Hubbard, G. Bishop, T. Gowen, D. Hayter, and G. Innes, “Multispectral-polarimetric sensing for detection of difficult targets,” Proc. SPIE Europe Security and Defence, 71130L (2008).

Y. Schechner and S. K. Nayar, “Polarization mosaicing: high dynamic range and polarization imaging in a wide field of view,” Proc. SPIE 48th Annual Meeting of Optical Science and Technology, 93–102 (2003).

P. Porral, P. Callet, P. Fuchs, T. Muller, and E. Sandré-Chardonnal, “High Dynamic, Spectral, and Polarized Natural Light Environment Acquisition,” Proceedings of SPIE/IS&T Electronic Imaging, 94030B (2015).

S. Mann and R. Picard, “Being undigital with digital cameras,” MIT Media Lab Perceptual (1994).

P. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in Proceedings of ACM SIGGRAPH pp. 31–40, (2008).

Y. Tsin, V. Ramesh, and T. Kanade, “Statistical calibration of CCD imaging process,” in Proceedings of Eighth International Conference on Computer Vision (ICCV) 1, pp. 480–487 (2001).

D. Arnaud, High Dynamic Range imaging: Sensors and Architectures (SPIE, 2012).

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Figures (12)

Fig. 1
Fig. 1 Left: Tone-mapped version of monochrome HDR image of the scene used for radiometric calibration. The scale shows scene radiance (W/m2sr) in a logarithmic scale. Right: spectral radiances of highlighted areas. The vertical axis is in logarithmic scale.
Fig. 2
Fig. 2 Left: 12 bits Camera Response Functions (CRF) for different aperture settings. Both axes are displayed in logarithmic scale. Only the sensor response range above noise floor and below saturation is shown. X axis in (W · s/sr · m2). Right: comparison between radiances measured with the spectroradiometer (black) and estimated from the HDR radiance map (red). The vertical axis is in logarithmic scale.
Fig. 3
Fig. 3 Left: Top: front view of imaging system with different LCTF angles. Bottom: overall imaging system view. Right: workflow diagram of the capture process.
Fig. 4
Fig. 4 Work-flow diagram of the whole image processing pipeline from capture to the final classification step.
Fig. 5
Fig. 5 Left: Weighting function used to scale the initial exposure time values to run the AEE algorithm in each spectral band. Values around [560 nm, 600 nm] are non-zero values. Right: Weighting function used to build HDR images from multiple 12-bits LDR images.
Fig. 6
Fig. 6 Image overlay 0° over 135° for 550 nm. Left: before registration. Right: after registration.
Fig. 7
Fig. 7 Segmentation algorithm.
Fig. 8
Fig. 8 Left: RGB1 image rendered. Right: RGB2 image after highlights removal.
Fig. 9
Fig. 9 Left: RGB2 image after highlights removal (a) and RGB3 image after the first iteration of mean-shift and labels2clusters processing (b). Right: Grayscale label images. Before region merging (c with 36 regions). After thresholding with th = 60 (d with 15 regions remaining), and after thresholding with th = 120 (e with 8 regions remaining).
Fig. 10
Fig. 10 Top row: RGB renderization of original spectral cubes. Bottom row: benchmark manually segmented. From left to right, scenes from 1 to 5.
Fig. 11
Fig. 11 a) Example of two HDR highlight surfaces (left) of a metal object, and DoLP surfaces (right). b) and c) 550 nm band images. The brightest point is highlighted with a red mark on the surfaces. The top example (b) is correctly classified as metal by the previously proposed method, but the bottom example (c) is not. d) Example of highlights detected (green) in scene 2 and their surrounding areas (blue). Each highlight is automatically classified as metal (M) or dielectric (D), according to its ratio value (green text). Proposed method using a threshold value of 1.2. e) Method in [14]
Fig. 12
Fig. 12 Left: Classification accuracy vs threshold value for training set. Rigth: DoLP ratio vs HDR Highlight radiance for the whole data set of highlights. Red: dielectric objects. Blue: metal objects.

Tables (1)

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Table 1 Jaccard index values for the three segmentation methods compared.

Equations (7)

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E λ θ ( x , y ) = n = 1 N ω ( ρ n λ θ ( x , y ) ) C R F 1 ( ρ n λ θ ( x , y ) ) Δ t n n = 1 N ω ( ρ n λ θ ( x , y ) )
ω i , j = k = 1 K ( ρ k , i ρ k , j ) 2
S 0 = I ( λ , θ ) + I ( λ , θ + 90 ° )
S 1 = I ( λ , θ ) I ( λ , θ + 90 ° )
S 2 = I ( λ , θ + 45 ° ) I ( λ , θ + 135 ° )
D o L P ( λ ) = s 1 2 + s 2 2 s 0
R = n = 1 N δ n h l m = 1 N δ m s u M N

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