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Shuai Han 0006

dblp:59/9242-6 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2014
0000-0001-5101-4953ORCID · corroborated

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

Artificial intelligence and machine learning · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author

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
2 papers
Computational photography and imaging · 70% Image and video processing · 30%
Computer networks
1 paper
Physical-layer communications · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging › reflectance acquisition
spectral reflectance estimation
0.212014
Fast Spectral Reflectance Recovery Using DLP Projector · Int. J. Comput. Vis. 2014
Image and video processing › color image processing
color correction
0.112012
Camera spectral sensitivity estimation from a single image under unknown illumination by using fluorescence · CVPR 2012
Physical-layer communications › signal processing for communications › spectral analysis
spectral estimation
0.012012
Camera spectral sensitivity estimation from a single image under unknown illumination by using fluorescence · CVPR 2012

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

single-image estimation · 0.3fluorescence chromaticity invariance · 0.3spectral imaging · 0.2DLP projector · 0.2
YearPublicationVenuePosition
2014 Fast Spectral Reflectance Recovery Using DLP Projector
Shuai Han 0006, Imari Sato, Takahiro Okabe, Yoichi Sato 0001
Int. J. Comput. Vis.1
2012 Camera spectral sensitivity estimation from a single image under unknown illumination by using fluorescence
abstract
Camera spectral sensitivity plays an important role for various color-based computer vision tasks. Although several methods have been proposed to estimate it, their applicability is severely restricted by the requirement for a known illumination spectrum. In this work, we present a single-image estimation method using fluorescence with no requirement for a known illumination spectrum. Under different illuminations, the spectral distributions of fluorescence emitted from the same material remain unchanged up to a certain scale. Thus, a camera's response to the fluorescence would have the same chromaticity. Making use of this chromaticity invariance, the camera spectral sensitivity can be estimated under an arbitrary illumination whose spectrum is unknown. Through extensive experiments, we proved that our method is accurate under different illuminations. Moreover, we show how to recover the spectra of daylight from the estimated results. Finally, we use the estimated camera spectral sensitivities and daylight spectra to solve color correction problems.
Shuai Han 0006, Yasuyuki Matsushita, Imari Sato, Takahiro Okabe, Yoichi Sato 0001
CVPR1
2010 Fast Spectral Reflectance Recovery Using DLP Projector
Shuai Han 0006, Imari Sato, Takahiro Okabe, Yoichi Sato 0001
ACCV (1)1