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Erschienen in:
Angry Crowds: Detecting Violent Events in Videos Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Sparse Recovery of Hyperspectral Signal from Natural RGB Images

verfasst von : Boaz Arad, Ohad Ben-Shahar

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Hyperspectral imaging is an important visual modality with growing interest and range of applications. The latter, however, is hindered by the fact that existing devices are limited in either spatial, spectral, and/or temporal resolution, while yet being both complicated and expensive. We present a low cost and fast method to recover high quality hyperspectral images directly from RGB. Our approach first leverages hyperspectral prior in order to create a sparse dictionary of hyperspectral signatures and their corresponding RGB projections. Describing novel RGB images via the latter then facilitates reconstruction of the hyperspectral image via the former. A novel, larger-than-ever database of hyperspectral images serves as a hyperspectral prior. This database further allows for evaluation of our methodology at an unprecedented scale, and is provided for the benefit of the research community. Our approach is fast, accurate, and provides high resolution hyperspectral cubes despite using RGB-only input.

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Vorheriges Kapitel Angry Crowds: Detecting Violent Events in Videos
Nächstes Kapitel Light Field Segmentation Using a Ray-Based Graph Structure
Fußnoten
1
After all, RGB samples are indeed 3 dimensional and it is quite unlikely, and never happened in our experiments, that \(D_{rbg}\) will not span RGB space \(\mathbb {R}^3\).
 
Literatur
1.
Kerekes, J., Schott, J.: Hyperspectral imaging systems. Hyperspectral data exploitation: theory and applications (2007)
2.
Lillesand, T., Kiefer, R., Chipman, J., et al.: Remote Sensing and Image Interpretation. Wiley, New York (2004)
3.
Haboudane, D., Miller, J., Pattey, E., Zarco-Tejada, P., Strachan, I.: Hyperspectral vegetation indices and novel algorithms for predicting green lai of crop canopies: modeling and validation in the context of precision agriculture. In: Remote Sensing of Environment (2004)
4.
Cloutis, E.: Review article hyperspectral geological remote sensing: evaluation of analytical techniques. Int. J. Remote Sens. 17, 2215–2242 (1996)CrossRef
5.
Hege, E., O’Connell, D., Johnson, W., Basty, S., Dereniak, E.: Hyperspectral imaging for astronomy and space surviellance. In: SPIE (2004)
6.
Mustard, J., Sunshine, J.: Spectral analysis for earth science: investigations using remote sensing data. In: Manual of Remote Sensing, Remote Sensing for the Earth Sciences (1999)
7.
Green, R., Eastwood, M., Sarture, C., Chrien, T., Aronsson, M., Chippendale, B., Faust, J., Pavri, B., Chovit, C., Solis, M., Olah, M., Williams, O.: Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). In: Remote Sensing of Environment (1998)
8.
James, J.: Spectrograph Design Fundamentals. Cambridge University Press, New York (2007)CrossRef
9.
Descour, M., Dereniak, E.: Computed-tomography imaging spectrometer: experimental calibration and reconstruction results. Appl. Opt. 34, 4817–4826 (1995)CrossRef
10.
Okamoto, T., Yamaguchi, I.: Simultaneous acquisition of spectral image information. Opt. Lett. 16, 1277–1279 (1991)CrossRef
11.
Johnson, W., Wilson, D., Bearman, G.: Spatial-spectral modulating snapshot hyperspectral imager. Appl. Opt. 45, 1898–1908 (2006)CrossRef
12.
Brady, D., Gehm, M.: Compressive imaging spectrometers using coded apertures. In: Defense and Security Symposium (2006)
13.
Gehm, M., John, R., Brady, D., Willett, R., Schulz, T.: Single-shot compressive spectral imaging with a dual-disperser architecture. Opt. Express 15, 14013–14027 (2007)CrossRef
14.
Lin, X., Wetzstein, G., Liu, Y., Dai, Q.: Dual-coded compressive hyperspectral imaging. Opt. Lett. 39, 2044–2047 (2014)CrossRef
15.
Fletcher-Holmes, D., Harvey, A.: Real-time imaging with a hyperspectral fovea. J. Opt. A Pure Appl. Opt. 7, S298–S302 (2005)CrossRef
16.
Wang, T., Zhu, Z., Rhody, H.: A smart sensor with hyperspectral/range fovea and panoramic peripheral view. In: CVPR (2009)
17.
Du, H., Tong, X., Cao, X., Lin, S.: A prism-based system for multispectral video acquisition. In: ICCV (2009)
18.
Kawakami, R., Wright, J., Yu-Wing, T., Matsushita, Y., Ben-Ezra, M., Ikeuchi, K.: High-resolution hyperspectral imaging via matrix factorization. In: CVPR (2011)
19.
Cao, X., Tong, X., Dai, Q., Lin, S.: High resolution multispectral video capture with a hybrid camera system. In: CVPR (2011)
20.
Goel, M., Whitmire, E., Mariakakis, A., Saponas, T.S., Joshi, N., Morris, D., Guenter, B., Gavriliu, M., Borriello, G., Patel, S.N.: Hypercam: hyperspectral imaging for ubiquitous computing applications (2015)
21.
Parmar, M., Lansel, S., Wandell, B.A.: Spatio-spectral reconstruction of the multispectral datacube using sparse recovery. In: ICIP (2008)
22.
Kohonen, O., Parkkinen, J., Jääskeläinen, T.: Databases for spectral color science. Color Res. Appl. 31, 381–390 (2006)CrossRef
23.
24.
Brelstaff, G., Párraga, A., Troscianko, T., Carr, D.: Hyperspectral camera system: acquisition and analysis. In: SPIE (1995)
25.
Foster, D., Amano, K., Nascimento, S., Foster, M.: Frequency of metamerism in natural scenes. JOSA A 23, 2359–2372 (2006)CrossRef
26.
Yasuma, F., Mitsunaga, T., Iso, D., Nayar, S.: Generalized assorted pixel camera: post-capture control of resolution, dynamic range and spectrum. Technical report (2008)
27.
Chakrabarti, A., Zickler, T.: Statistics of real-world hyperspectral images. In: CVPR (2011)
28.
29.
Palmer, S.: Vision Science: Photons to Phenomenology. The MIT Press, Cambrdige (1999)
30.
Cohen, J.: Dependency of the spectral reflectance curves of the Munsell color chips. Psychonomic Sci. 1, 369–370 (1964)CrossRef
31.
Maloney, L.: Evaluation of linear models of surface spectral reflectance with small numbers of parameters. JOSA A 3, 1673–1683 (1986)CrossRef
32.
Parkkinen, J.P., Hallikainen, J., Jaaskelainen, T.: Characteristic spectra of Munsell colors. JOSA A 6, 318–322 (1989)CrossRef
33.
Hardeberg, J.Y.: On the spectral dimensionality of object colors. In: Proceedings of CGIV 2002, First European Conference on Colour in Graphics (2002)
34.
Adams, J., Smith, M., Gillespie, A.: Simple models for complex natural surfaces: a strategy for the hyperspectral era of remote sensing. In: IGARSS (1989)
35.
Heikkinen, V., Lenz, R., Jetsu, T., Parkkinen, J., Hauta-Kasari, M., Jääskeläinen, T.: Evaluation and unification of some methods for estimating reflectance spectra from RGB images. JOSA A 25, 2444–2458 (2008)CrossRef
36.
López-Álvarez, M.A., Hernández-Andrés, J., Romero, J., Olmo, F., Cazorla, A., Alados-Arboledas, L.: Using a trichromatic CCD camera for spectral skylight estimation. Appl. Opt. 47, 31–38 (2008)CrossRef
37.
Ayala, F., Echávarri, J.F., Renet, P., Negueruela, A.I.: Use of three tristimulus values from surface reflectance spectra to calculate the principal components for reconstructing these spectra by using only three eigenvectors. JOSA A (2006)
38.
Xing, Z., Zhou, M., Castrodad, A., Sapiro, G., Carin, L.: Dictionary learning for noisy and incomplete hyperspectral images. SIAM J. Imaging Sci. 5, 33–56 (2012)MathSciNetCrossRefMATH
39.
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)CrossRef
40.
Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers (1993)
41.
Jiang, J., Liu, D., Gu, J., Susstrunk, S.: What is the space of spectral sensitivity functions for digital color cameras? In: WACV, IEEE (2013)
Metadaten
Titel
Sparse Recovery of Hyperspectral Signal from Natural RGB Images
verfasst von
Boaz Arad
Ohad Ben-Shahar
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46477-0

Electronic ISBN: 978-3-319-46478-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46478-7_2