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Your search keyword '"Sugano, Yusuke"' showing total 14 results
Start Over Author "Sugano, Yusuke" Publication Type Books
14 results on '"Sugano, Yusuke"'

3. List of Contributors

Author

Ackermann, Hanno, primary, Cao, Yanlong, additional, Cao, Yanpeng, additional, Chehata, Nesrine, additional, Cremers, Daniel, additional, Garcia, Nuno C., additional, Gibert, Jaume, additional, Gomez, Lluis, additional, Gomez, Raul, additional, Gouet-Brunet, Valerie, additional, Guan, Dayan, additional, Huang, Hai, additional, Iwaszczuk, Dorota, additional, Kanezaki, Asako, additional, Karatzas, Dimosthenis, additional, Kniaz, Vladimir V., additional, Knyaz, Vladimir A., additional, Koppanyi, Zoltan, additional, Kuga, Ryohei, additional, Kuhn, Andreas, additional, Le Bris, Arnaud, additional, Mallet, Clément, additional, Matsushita, Yasuyuki, additional, Mayer, Helmut, additional, Michelini, Mario, additional, Morerio, Pietro, additional, Murino, Vittorio, additional, Ojail, Maroun, additional, Ouerghemmi, Walid, additional, Piriou, Erwan, additional, Poreba, Martyna, additional, Postadjian, Tristan, additional, Puissant, Anne, additional, Reinders, Christoph, additional, Rosenhahn, Bodo, additional, Salhi, Imane, additional, Saul, Can Jozef, additional, Schmitz, Matthias, additional, Sugano, Yusuke, additional, Toth, Charles K., additional, Usenko, Vladyslav, additional, von Stumberg, Lukas, additional, Wendl, Cyril, additional, Yang, Jiangxin, additional, Yang, Michael Ying, additional, Yilmaz, Alper, additional, and Zha, Bing, additional

Published
2019
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13. An Incremental Learning Method for Unconstrained Gaze Estimation.

Author

Sugano, Yusuke, Matsushita, Yasuyuki, Sato, Yoichi, and Koike, Hideki

Abstract

This paper presents an online learning algorithm for appea- rance-based gaze estimation that allows free head movement in a casual desktop environment. Our method avoids the lengthy calibration stage using an incremental learning approach. Our system keeps running as a background process on the desktop PC and continuously updates the estimation parameters by taking user΄s operations on the PC monitor as input. To handle free head movement of a user, we propose a pose-based clustering approach that efficiently extends an appearance manifold model to handle the large variations of the head pose. The effectiveness of the proposed method is validated by quantitative performance evaluation with three users. [ABSTRACT FROM AUTHOR]

Published
2008
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14. Person-Independent Monocular Tracking of Face and Facial Actions with Multilinear Models.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Zhou, S. Kevin, Zhao, Wenyi, Tang, Xiaoou, Gong, Shaogang, and Sugano, Yusuke

Abstract

In tracking face and facial actions of unknown people, it is essential to take into account two components of facial shape variations: shape variation between people and variation caused by different facial actions such as facial expressions. This paper presents a monocular method of tracking faces and facial actions using a multilinear face model that treats interpersonal and intrapersonal shape variations separately. We created this method using a multilinear face model by integrating two different frameworks: particle filter-based tracking for time-dependent facial action and pose estimation and incremental bundle adjustment for person-dependent shape estimation. This unique combination together with multilinear face models is the key to tracking faces and facial actions of arbitrary people in real time with no pre-learned individual face models. Experiments using real video sequences demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Published
2007
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