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Yusuke Bou

dblp:182/2258 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

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

Artificial intelligence and machine learning · 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
Image and video processing · 77% Geometric modeling and processing · 23%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d reconstruction
0.312018
Multiview Rectification of Folded Documents · IEEE Trans. Pattern Anal. Mach. Intell. 2018
Image and video processing
document image analysis
0.312018
Multiview Rectification of Folded Documents · IEEE Trans. Pattern Anal. Mach. Intell. 2018
Image and video processing › image restoration › document image restoration
document image rectification
0.312018
Multiview Rectification of Folded Documents · IEEE Trans. Pattern Anal. Mach. Intell. 2018
Geometric modeling and processing › surface parameterization
conformal mapping
0.112018
Multiview Rectification of Folded Documents · IEEE Trans. Pattern Anal. Mach. Intell. 2018
Geometric modeling and processing › surface parameterization
UV mapping
0.112018
Multiview Rectification of Folded Documents · IEEE Trans. Pattern Anal. Mach. Intell. 2018

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

ridge-aware 3d reconstruction · 0.7conformal mapping · 0.7
YearPublicationVenuePosition
2018 Multiview Rectification of Folded Documents
abstract
Digitally unwrapping images of paper sheets is crucial for accurate document scanning and text recognition. This paper presents a method for automatically rectifying curved or folded paper sheets from a few images captured from multiple viewpoints. Prior methods either need expensive 3D scanners or model deformable surfaces using over-simplified parametric representations. In contrast, our method uses regular images and is based on general developable surface models that can represent a wide variety of paper deformations. Our main contribution is a new robust rectification method based on ridge-aware 3D reconstruction of a paper sheet and unwrapping the reconstructed surface using properties of developable surfaces via conformal mapping. We present results on several examples including book pages, folded letters and shopping receipts.
Shaodi You, Yasuyuki Matsushita, Sudipta N. Sinha, Yusuke Bou, Katsushi Ikeuchi
IEEE Trans. Pattern Anal. Mach. Intell.4