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Tomohiro Yendo
Researcher at Nagoya University
Publications - 19
Citations - 327
Tomohiro Yendo is an academic researcher from Nagoya University. The author has contributed to research in topics: View synthesis & Depth map. The author has an hindex of 9, co-authored 19 publications receiving 316 citations. Previous affiliations of Tomohiro Yendo include Nagaoka University of Technology.
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Journal ArticleDOI
The Seelinder: Cylindrical 3D display viewable from 360 degrees
TL;DR: A 3D video display technique that allows multiple viewers to see 3D images from a 360-degree horizontal arc without wearing 3D glasses is proposed and improved by revolving the parallax barrier.
Proceedings ArticleDOI
High-speed-camera image processing based LED traffic light detection for road-to-vehicle visible light communication
H. Chinthaka N. Premachandra,Tomohiro Yendo,Mehrdad Panahpour Tehrani,Takaya Yamazato,Hiraku Okada,Toshiaki Fujii,Masayuki Tanimoto +6 more
TL;DR: New effective algorithms for finding and tracking the transmitter are proposed, which result in a increased communication speed, compared to the previous methods.
Journal ArticleDOI
Artifact reduction using reliability reasoning for image generation of FTV
TL;DR: A new view synthesis method in multiview camera configurations of Free viewpoint TV (FTV) where potential depth errors are considered and the complementarity principle of the artifacts from left and right references is infers.
Proceedings ArticleDOI
Probabilistic reliability based view synthesis for FTV
TL;DR: A probabilistic framework which constrains the reliability of each pixel of new view by Maximizing Likelihood (ML) is introduced, and the virtual view is generated by solving a Maximum a Posterior (MAP) problem using graph cuts.
Proceedings ArticleDOI
Novel view synthesis with residual error feedback for FTV
Hisayoshi Furihata,Tomohiro Yendo,Mehrdad Panahpour Tehrani,Toshiaki Fujii,Masayuki Tanimoto +4 more
TL;DR: A new method of DIBR using multi-view images acquired in a linear camera arrangement that improves virtual viewpoint images by predicting the residual errors and in the experiments, PSNR could be improved for few decibels compared with the conventional method.