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研究員
劉庭祿 Tyng-Luh Liu

Research Fellow
Ph.D., Computer Science, New York University
Tel: +886-2-2788-3799 ext. 1508 Fax: +886-2-2782-4814
Email: liutyng@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/liutyng

● Academia Sinica Young Investi- Research Description
gator Award, Academia Sinica
(2006)
My research has focused on computer vision and machine learning techniques that sup-
● Visiting Scholar, School of Electri- port the related applications. Speci cally, I am most interested in understanding the fun-

cal and Computer Engineering,
Cornell University (2013-14) damentals, and addressing critical issues in realizing object detection, recognition, and
scene understanding. In view of that the aforementioned applications in computer vision
● Postdoc, Courant Institute of
Mathematical Sciences (1997-98) rely heavily on the underlying imaging devices such as RGBD and ego-centric cameras, my

research e orts also take this perspective into account in designing computer vision tech-
niques, ranging from low-level to high-level, to more appropriately exploit the available
imaging information about the scene and its contents. In addition, generalizing conven-

tional computer vision methods to more e ectively deal with large-scale image data will
continue to play an important role in my current research.

Publications

1. Kai-Yueh Chang, Tyng-Luh Liu, Hwann-Tzong Chen, and sion, vol. 65, no. 1-2, pp. 73-96, November 2005.
Shang-Hong Lai, “Fusing Generic Objectness and Visual Sa- 7. Yen-Yu Lin, Tyng-Luh Liu, and Hwann-Tzong Chen, “Se-
liency for Salient Object Detection,” International Conference
mantic Manifold Learning for Image Retrieval,” ACM Inter-
on Computer Vision, Barcelona, Spain, November 2011.
national Conference on Multimedia, pp. 249-258, Singapore,
2. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, “Multiple November 2005.
Kernel Learning for Dimensionality Reduction,” IEEE Trans-
actions on Pattern Analysis and Machine Intelligence, vol. 33, 8. Hwann-Tzong Chen, Huang-Wei Chang, and Tyng-Luh Liu,
no. 6, pp. 1147-1160, 2011. “Local Discriminant Embedding and Its Variants,” IEEE
Computer Society International Conference on Computer Vi-
3. Kai-Yueh Chang, Tyng-Luh Liu, and Shang-Hong Lai. Learn- sion and Pattern Recognition, vol. 1, pp. 679-686, San Diego,
ing Partially-Observed Hidden Conditional Random Fields for CA, USA, June 2005.
Facial Expression Recognition,” IEEE Computer Society In-
ternational Conference on Computer Vision and Pattern Rec- 9. Yen-Yu Lin and Tyng-Luh Liu, “Robust Face Detection with
ognition, Miami, FL, USA, June 2009. Multi-Class Boosting,” IEEE Computer Society International
Conference on Computer Vision and Pattern Recognition, vol.
4. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, “Dimen- 1, pp. 679-686, San Diego, CA, USA, June 2005.
sionality Reduction for Data in Multiple Feature Representa-
10. Tyng-Luh Liu and Hwann-Tzong Chen, “Real-Time Tracking
tions,” Advances in Neural Information Processing Systems
Using Trust-Region Methods,” IEEE Transactions on Pattern
21, edited by D. Koller, Y. Bengio, D. Schuurmans, L. Bot-
Analysis and Machine Intelligence, vol. 26, no. 3, pp. 397-
tou, and A. Culotta, pp. 961-968, MIT Press, Cambridge MA,
402, March 2004.
2009.
5. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, “Local
Ensemble Kernel Learning for Object Category Recognition,”
IEEE Computer Society International Conference on Comput-
er Vision and Pattern Recognition, Minneapolis, MN, USA,
June 2007.
6. Hwann-Tzong Chen, Tyng-Luh Liu, and Chiou-Shann Fuh,
“Tone Reproduction: A Perspective from Luminance-Driven
Perceptual Grouping,” International Journal of Computer Vi-

88 研究人員 Research Faculty