Abstract
Hyperspectral imaging has become more accessible nowadays as an image-based acquisition tool for physically-meaningful measurements. This technology is now evolving from classical 2D imaging to 3D imaging, allowing us to measure physically-meaningful reflectance on 3D solid objects. This chapter provides a brief overview on the foundations of hyperspectral imaging and introduces advanced applications of hyperspectral 3D imaging. This chapter first surveys the fundamentals of optics and calibration processes of hyperspectral imaging and then studies two typical designs of hyperspectral imaging. In addition to this introduction, this chapter briefly looks over the state-of-the-art applications of hyperspectral 3D imaging to measure hyperspectral intrinsic properties of surfaces on 3D solid objects.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Attas M, Cloutis E, Collins C, Goltz D, Majzels C, Mansfield JR, Mantsch HH. Near-infrared spectroscopic imaging in art conservation: investigation of drawing constituents. J Cult Herit. 2003;4(2):127–36.
Barsky S, Petrou M. The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Trans Pattern Anal Mach Intell. 2003;25(10):1239–52.
Basri R, Jacobs DW, Kemelmacher I. Photometric stereo with general, unknown lighting. Int J Comput Vis. 2007;72(3):239–57.
Bernardini F, Rushmeier H. The 3D model acquisition pipeline. Comput Graph Forum. 2002;21(2):149.
Brauers J, Schulte N, Aach T. Modeling and compensation of geometric distortions of multispectral cameras with optical bandpass filter wheels. In: 15th European signal processing conference, vol. 15; 2007. p. 1902–6.
Buchsbaum G. A spatial processor model for object colour perception. J Franklin Inst. 1980;310(1):1–26.
Chandraker M, Agarwal S, Kriegman D. Shadowcuts: photometric stereo with shadows. In: IEEE conference on Computer Vision and Pattern Recognition, 2007 (CVPR’07). Piscataway: IEEE, p. 1–8.
CIE. Colorimetry. CIE Pub. 15.2, Commission Internationale de l’Eclairage (CIE), Vienna; 1986.
Du H, Tong X, Cao X, Lin S. A prism-based system for multispectral video acquisition. In: Proceedings of the International Conference on Computer Vision (ICCV), Piscataway: IEEE, 2009. p. 175–82.
Farouk M, Rifai IE, Tayar SE, Shishiny HE, Hosny M, Rayes ME, Gomes J, Giordano F, Rushmeier HE, Bernardini F, Magerlein K. Scanning and processing 3D objects for web display. In: Proceedings of the international conference on 3D Digital Imaging and Modeling (3DIM); 2003. p. 310–7.
Habel R, Kudenov M, Wimmer M. Practical spectral photography. In: Computer graphics forum, vol. 31. Wiley-Blackwell: Hoboken, 2012. p. 449–58.
Hardeberg JY, Schmitt F, Brettel H. Multispectral color image capture using a liquid crystal tunable filter. Opt Eng. 2002;41(10):2532–48.
Hernández C, Vogiatzis G, Cipolla R. Shadows in three-source photometric stereo. In: Computer vision-ECCV 2008. Springer; 2008. p. 290–303.
Holroyd M, Lawrence J, Zickler T. A coaxial optical scanner for synchronous acquisition of 3D geometry and surface reflectance. ACM Trans Graph (Proc SIGGRAPH 2010). Los Angeles, United States, 2010;29(3):1–12. Article no. 99.
Hoye G, Fridman A. Mixel camera—a new push-broom camera concept for high spatial resolution keystone-free hyperspectral imaging. Opt Exp. 2013;21(9):11,057–77.
Hyper3D. An open-source project in SourceForge.net. 2012. http://sourceforge.net/projects/hyper3d/.
Kawakami R, Wright J, Tai YW, Matsushita Y, Ben-Ezra M, Ikeuchi K. High-resolution hyperspectral imaging via matrix factorization. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2011. p. 2329–36.
Kim MH, Harvey TA, Kittle DS, Rushmeier H, Dorsey J, Prum RO, Brady DJ. 3D imaging spectroscopy for measuring hyperspectral patterns on solid objects. ACM Trans Graph (Proc SIGGRAPH 2014). 2012;31(4):38:1–11.
Kim MH, Rushmeier H, ffrench J, Passeri I. Developing open-source software for art conservators. In: The international symposium on virtual reality, archaeology and intelligent cultural heritage, eurographics association. Brighton, England; 2012. p. 97–104.
Kim MH, Rushmeier H, ffrench J, Passeri I, Tidmarsh D. Hyper3D: 3D graphics software for examining cultural artifacts. ACM J Comput Cult Herit 2014;7(3):1:1–19.
Kittle D, Choi K, Wagadarikar A, Brady DJ. Multiframe image estimation for coded aperture snapshot spectral imagers. Appl Opt 2010;49(36):6824–33.
Lee H, Kim MH. Building a two-way hyperspectral imaging system with liquid crystal tunable filters. In: Springer LNCS 8509 (Proceedings of ICISP 2014). Normandy: Springer; 2014. p. 26–34.
Liao M, Huang X, Yang R. Interreflection removal for photometric stereo by using spectrum-dependent albedo. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR); 2011. p. 689–96.
Lucas Digital Ltd. OpenEXR. 2009. http://www.openexr.com/.
Mansouri A, Lathuiliere A, Marzani FS, Voisin Y, Gouton P. Toward a 3d multispectral scanner: an application to multimedia. MultiMedia, IEEE. 2007;14(1):40–7.
Mouroulis P, Green RO, Chrien TG. Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information. Appl Opt. 2000;39(13):2210–20.
Nam G, Kim MH. Multispectral photometric stereo for acquiring high-fidelity surface normals. IEEE Comput Graph Appl. 2014;34(6):57–68.
Nayar SK, Ikeuchi K, Kanade T. Shape from interreflections. Int J Comput Vis (IJCV). 1991;6(3):173–95.
Nielsen M, Stokes M. The creation of the sRGB ICC profile. In: Proceedings of the color imaging conference IS&T; 1998. p. 253–7.
Qin J. Hyperspectral imaging instruments. In: Sun DW, editor. Hyperspectral imaging for food quality analysis and control. Elsevier: Amsterdam, 2010. p. 129–75.
Rapantzikos K, Balas C. Hyperspectral imaging: potential in non-destructive analysis of palimpsests. In: Proceedings of the International Conference on Image Processing (ICIP), vol. 2; 2005. p. 618–21.
Rapantzikos K, Balas C. Hyperspectral imaging: potential in non-destructive analysis of palimpsests. In: IEEE International Conference on Image Processing, 2005 (ICIP 2005), vol. 2. Piscataway: IEEE, 2005. p. II–618.
Sugiura H, Kuno T, Watanabe N, Matoba N, Hayashi J, Miyata Y. Development of a multispectral camera system. Proc SPIE. 2000;3965:331–9.
Sun J, Smith M, Smith L, Midha S, Bamber J. Object surface recovery using a multi-light photometric stereo technique for non-lambertian surfaces subject to shadows and specularities. Image Vis Comput. 2007;25(7):1050–7.
Takatani T, Matsushita Y, Lin S, Mukaigawa Y, Yagi Y. Enhanced photometric stereo with multispectral images. In: International conference on Machine Vision Applications (MVA). IAPR: Kyoto, 2013.
Verbiest F, Van Gool L. Photometric stereo with coherent outlier handling and confidence estimation. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR); 2008. p. 1–8.
Vogiatzis G, Hernández C. Self-calibrated, multi-spectral photometric stereo for 3d face capture. Int J Comput Vis (IJCV). 2012;97(1):91–103.
Wagadarikar A, John R, Willett R, Brady DJ. Single disperser design for coded aperture snapshot spectral imaging. Appl Opt. 2008;47(10):B44–51.
Wagadarikar AA, Pitsianis NP, Sun X, Brady DJ. Video rate spectral imaging using a coded aperture snapshot spectral imager. Opt Exp. 2009;17(8):6368–88.
Ware G, Chabries D, Christiansen R, Brady J, Martin C. Multispectral analysis of ancient Maya pigments: implications for the Naj Tunich Corpus. In: Proceedings of the IEEE geoscience and remote sensing symposium, vol. 6; 2000. p. 2489–91.
Wu TP, Tang KL, Tang CK, Wong TT. Dense photometric stereo: a markov random field approach. IEEE Trans Pattern Anal Mach Intell. 2006;28(11):1830–46.
Acknowledgements
This work is supported by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as the Global Frontier Project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Kim, M.H. (2015). The Three-Dimensional Evolution of Hyperspectral Imaging. In: Lin, YL., Kyung, CM., Yasuura, H., Liu, Y. (eds) Smart Sensors and Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-14711-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-14711-6_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14710-9
Online ISBN: 978-3-319-14711-6
eBook Packages: EngineeringEngineering (R0)