Page 86 - 2017 Brochure
P. 86
究員
劉庭祿 Tyng-Luh Liu
Research Fellow
Ph.D., Computer Science, New York University
Tel: +886-2-2788-3799 ext. 1508 or 1551 Fax: +886-2-2789-3309
Email: liutyng@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/liutyng/
• Academia Sinica Young Investigator Award (2006)
• Visiting Scholar, School of Electrical and Computer Engineering, Cornell University (2013-14)
• Postdoc, Courant Institute of Mathematical Sciences, New York University (1997-98)
Research Description
My research has focused on computer vision and machine learning
techniques that support the related applications. Specifically,
I am most interested in understanding the fundamentals, and
addressing critical issues in realizing scene understanding, vision
and language. In view of that the aforementioned applications rely
heavily on the underlying imaging devices such as 360° and ego-
centric cameras, my research efforts also take this perspective into
account in designing computer vision techniques, ranging from
low-level to high-level, to more appropriately exploit the available
imaging information about the scene and its contents. In addition,
generalizing conventional computer vision methods to more
effectively deal with large-scale image data will continue to play an
important role in my current research.
Publications 6. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, “Local Ensemble
Kernel Learning for Object Category Recognition,” IEEE Computer
1. Tsung-Wei Ke, Che-Wei Lin, Tyng-Luh Liu and Davi Geiger, Society International Conference on Computer Vision and Pattern
“Variational Convolutional Networks for Human-Centric Recognition, Minneapolis, MN, USA, June 2007.
Annotations,” Asian Conference on Computer Vision, Taipei, Taiwan,
November 2016. 7. Hwann-Tzong Chen, Tyng-Luh Liu, and Chiou-Shann Fuh, “Tone
Reproduction: A Perspective from Luminance-Driven Perceptual
2. Kai-Yueh Chang, Tyng-Luh Liu, Hwann-Tzong Chen, and Shang- Grouping,” International Journal of Computer Vision, vol. 65, no. 1-2,
Hong Lai, “Fusing Generic Objectness and Visual Saliency for Salient pp. 73-96, November 2005.
Object Detection,” International Conference on Computer Vision,
Barcelona, Spain, November 2011. 8. Yen-Yu Lin, Tyng-Luh Liu, and Hwann-Tzong Chen, “Semantic
Manifold Learning for Image Retrieval,” ACM International
3. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, “Multiple Kernel Conference on Multimedia, pp. 249-258, Singapore, November 2005.
Learning for Dimensionality Reduction,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1147- 9. Hwann-Tzong Chen, Huang-Wei Chang, and Tyng-Luh Liu, “Local
1160, 2011. Discriminant Embedding and Its Variants,” IEEE Computer Society
International Conference on Computer Vision and Pattern Recognition,
4. Kai-Yueh Chang, Tyng-Luh Liu, and Shang-Hong Lai. “Learning vol. 1, pp. 679-686, San Diego, CA, USA, June 2005.
Partially-Observed Hidden Conditional Random Fields for Facial
Expression Recognition,” IEEE Computer Society International 10. Tyng-Luh Liu and Hwann-Tzong Chen, “Real-Time Tracking Using
Conference on Computer Vision and Pattern Recognition, Miami, FL, Trust-Region Methods,” IEEE Transactions on Pattern Analysis and
USA, June 2009. Machine Intelligence, vol. 26, no. 3, pp. 397-402, March 2004.
5. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, “Dimensionality
Reduction for Data in Multiple Feature Representations,” Advances
in Neural Information Processing Systems 21, edited by D. Koller, Y.
Bengio, D. Schuurmans, L. Bottou, and A. Culotta, pp. 961-968, MIT
Press, Cambridge MA, 2009.
84 研究人員 Research Faculty
劉庭祿 Tyng-Luh Liu
Research Fellow
Ph.D., Computer Science, New York University
Tel: +886-2-2788-3799 ext. 1508 or 1551 Fax: +886-2-2789-3309
Email: liutyng@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/liutyng/
• Academia Sinica Young Investigator Award (2006)
• Visiting Scholar, School of Electrical and Computer Engineering, Cornell University (2013-14)
• Postdoc, Courant Institute of Mathematical Sciences, New York University (1997-98)
Research Description
My research has focused on computer vision and machine learning
techniques that support the related applications. Specifically,
I am most interested in understanding the fundamentals, and
addressing critical issues in realizing scene understanding, vision
and language. In view of that the aforementioned applications rely
heavily on the underlying imaging devices such as 360° and ego-
centric cameras, my research efforts also take this perspective into
account in designing computer vision techniques, ranging from
low-level to high-level, to more appropriately exploit the available
imaging information about the scene and its contents. In addition,
generalizing conventional computer vision methods to more
effectively deal with large-scale image data will continue to play an
important role in my current research.
Publications 6. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, “Local Ensemble
Kernel Learning for Object Category Recognition,” IEEE Computer
1. Tsung-Wei Ke, Che-Wei Lin, Tyng-Luh Liu and Davi Geiger, Society International Conference on Computer Vision and Pattern
“Variational Convolutional Networks for Human-Centric Recognition, Minneapolis, MN, USA, June 2007.
Annotations,” Asian Conference on Computer Vision, Taipei, Taiwan,
November 2016. 7. Hwann-Tzong Chen, Tyng-Luh Liu, and Chiou-Shann Fuh, “Tone
Reproduction: A Perspective from Luminance-Driven Perceptual
2. Kai-Yueh Chang, Tyng-Luh Liu, Hwann-Tzong Chen, and Shang- Grouping,” International Journal of Computer Vision, vol. 65, no. 1-2,
Hong Lai, “Fusing Generic Objectness and Visual Saliency for Salient pp. 73-96, November 2005.
Object Detection,” International Conference on Computer Vision,
Barcelona, Spain, November 2011. 8. Yen-Yu Lin, Tyng-Luh Liu, and Hwann-Tzong Chen, “Semantic
Manifold Learning for Image Retrieval,” ACM International
3. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, “Multiple Kernel Conference on Multimedia, pp. 249-258, Singapore, November 2005.
Learning for Dimensionality Reduction,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1147- 9. Hwann-Tzong Chen, Huang-Wei Chang, and Tyng-Luh Liu, “Local
1160, 2011. Discriminant Embedding and Its Variants,” IEEE Computer Society
International Conference on Computer Vision and Pattern Recognition,
4. Kai-Yueh Chang, Tyng-Luh Liu, and Shang-Hong Lai. “Learning vol. 1, pp. 679-686, San Diego, CA, USA, June 2005.
Partially-Observed Hidden Conditional Random Fields for Facial
Expression Recognition,” IEEE Computer Society International 10. Tyng-Luh Liu and Hwann-Tzong Chen, “Real-Time Tracking Using
Conference on Computer Vision and Pattern Recognition, Miami, FL, Trust-Region Methods,” IEEE Transactions on Pattern Analysis and
USA, June 2009. Machine Intelligence, vol. 26, no. 3, pp. 397-402, March 2004.
5. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, “Dimensionality
Reduction for Data in Multiple Feature Representations,” Advances
in Neural Information Processing Systems 21, edited by D. Koller, Y.
Bengio, D. Schuurmans, L. Bottou, and A. Culotta, pp. 961-968, MIT
Press, Cambridge MA, 2009.
84 研究人員 Research Faculty
