Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Yasuyuki Kobashi

dblp:151/8904 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Computational photography and imaging · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging › spectral imaging
hyperspectral imaging
0.212014
Reflectance and Fluorescent Spectra Recovery Based on Fluorescent Chromaticity Invariance under Varying Illumination · CVPR 2014
Computational photography and imaging › reflectance acquisition
spectral reflectance estimation
0.212014
Reflectance and Fluorescent Spectra Recovery Based on Fluorescent Chromaticity Invariance under Varying Illumination · CVPR 2014

Methods — techniques the papers use, named apart from their topics

chromaticity invariance · 0.2RGB camera imaging · 0.2
YearPublicationVenuePosition
2014 Reflectance and Fluorescent Spectra Recovery Based on Fluorescent Chromaticity Invariance under Varying Illumination
abstract
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 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 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.
Ying Fu 0001, Antony Lam, Yasuyuki Kobashi, Imari Sato, Takahiro Okabe, Yoichi Sato 0001
CVPR3