Spectral Modeling and Relighting of Reflective-Fluorescent Scenes

Lam, Antony; Sato, Iwao

doi:10.1109/cvpr.2013.191

Cited by 21 publications

(21 citation statements)

References 23 publications

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“…Previous work has also shown that absorption spectra can be well represented by basis functions [10]. In our investigation, we have found that a large collection of absorption spectra from the McNamara and Boswell Fluorescence Spectral Dataset [13] can be well represented using 9 principal components.…”

Section: Fluorescence Absorption Spectrum Recoverymentioning

confidence: 89%

“…In recent work, methods for hyperspectral imaging of reflective-fluorescent scenes have been proposed. Lam and Sato [10] provided a method for recovering the full spectral reflectance and fluorescence absorption and emission spectra of scenes but they require a multiband camera and multiple narrowband illuminants. Fu et al [4] also recovered the full spectral reflectance and fluorescence spectra of scenes by using high frequency light spectra but they require a hyperspectral camera and a programmable light source: a device that can be programmed to produce arbitrary light spectra.…”

Section: Related Workmentioning

confidence: 99%

“…Zhang and Sato [20] proposed a method that uses a RGB camera and ICA to separate reflective and fluorescent components but their method does not capture spectral distributions. In recent work, methods for hyperspectral imaging of reflective-fluorescent scenes have been proposed [10,4]. These methods are effective but require specialized cameras and specialized illuminants such as narrowband light or high frequency light spectra.…”

Section: Introductionmentioning

confidence: 99%

“…This is because fluorescence can provide additional information about scenes and has been applied to problems in camera spectral sensitivity estimation [5], 3D reconstruction [16,18], immersion range scanning [6], and color relighting [10,4] to name a few. In particular, hyperspec- tral images of reflective-fluorescent scenes provides a rich amount of data.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reflectance and Fluorescent Spectra Recovery Based on Fluorescent Chromaticity Invariance under Varying Illumination

Lam

Yasuyuki³

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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In recent years, fluorescence analysis of scenes has received attention. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluorescent scenes provide a rich amount of data. However, due to the complex nature of fluorescence, hyperspectral imaging methods rely on specialized equipment such as hyperspectral cameras and specialized illuminants. In this paper, we propose a more practical approach to hyperspectral imaging of reflectivefluorescent scenes using only a conventional RGB camera and varied colored illuminants. The key idea of our approach is to exploit a unique property of fluorescence: the chromaticity of fluorescence emissions are invariant under different illuminants. This allows us to robustly estimate spectral reflectance and fluorescence emission chromaticity. We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated. We demonstrate in results that all scene spectra can be accurately estimated from RGB images. Finally, we show that our method can be used to accurately relight scenes under novel lighting.

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Section: Fluorescence Absorption Spectrum Recoverymentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reflectance and Fluorescent Spectra Recovery Based on Fluorescent Chromaticity Invariance under Varying Illumination

Lam

Yasuyuki³

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

Self Cite

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show abstract

“…This is because fluorescence can provide additional information about scenes and has been applied to problems in camera spectral sensitivity estimation [1], 3D reconstruction [2], [3], immersion range scanning [4], and color relighting [5], [6] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Reflectance and Fluorescence Spectral Recovery via Actively Lit RGB Images

Lam

Sato

et al. 2016

IEEE Trans. Pattern Anal. Mach. Intell.

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In recent years, fluorescence analysis of scenes has received attention in computer vision. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluorescent scenes provide a rich amount of data. However, due to the complex nature of fluorescence, hyperspectral imaging methods rely on specialized equipment such as hyperspectral cameras and specialized illuminants. In this paper, we propose a more practical approach to hyperspectral imaging of reflective-fluorescent scenes using only a conventional RGB camera and varied colored illuminants. The key idea of our approach is to exploit a unique property of fluorescence: the chromaticity of fluorescent emissions are invariant under different illuminants. This allows us to robustly estimate spectral reflectance and fluorescent emission chromaticity. We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated. We demonstrate in results that all scene spectra can be accurately estimated from RGB images. Finally, we show that our method can be used to accurately relight scenes under novel lighting.

show abstract