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Research Faculty  研究人員


 研究員       副研究員
   劉庭祿 Tyng-Luh Liu    劉進興 Jing-Sin Liu



 Research Fellow  Associate Research Fellow
 Ph.D., Computer Science, New York University  Ph.D., Electrical Engineering, National Taiwan University


 Tel: +886-2-2788-3799 ext. 1508      Fax: +886-2-2782-4814  Tel: +886-2-2788-3799 ext. 1813      Fax: +886-2-2782-4814
 Email: liutyng@iis.sinica.edu.tw  Email: liu@iis.sinica.edu.tw
 http://www.iis.sinica.edu.tw/pages/liutyng  http://www.iis.sinica.edu.tw/pages/liu




   ● Research Fellow, IIS, Academia Sinica (2010 - )    ● Research Award for Junior Research Investigator, Academia     ● Associate Research Fellow, IIS, Academia Sinica (1994-present)    ● MS, Mechanical Engineering, National Taiwan University,1986
   ● Associate Research Fellow, IIS, Academia Sinica (2005 – 2010)  Sinica (2006)    ● Assistant Research Fellow, IIS, Academia Sinica (1990-1994)    ● BS, Mechanical Engineering, National Cheng-Kung Univer-
   ● Assistant Research Fellow, IIS, Academia Sinica (1998 – 2005)    ● Managing Editor, Journal of Information Science and Engineer-    ● Ph.D, Electrical Engineering, National Taiwan University, 1990  sity,1984
   ● Ph.D. Computer Science, New York University (1997)  ing (2010 - )

 Research Description  Publications  Research Description       Publications


 My research has focused on computer vision and pattern   1.  Kai-Yueh Chang,  Tyng-Luh Liu, Hwann-Tzong Chen, and   We face the challenges of making robots as both a good   1.  Ju M. Y., Liu J. S., Shiang S. P., Chien Y. R., Hwang K. S., and

 recognition. Speci cally, I am most interested in the topics   Shang-Hong Lai. “Fusing Generic Objectness and Visual Sali-  partner and a good helper to human daily life in the near   Lee W. C., 2001,“Fast and accurate collision detection based
 th
 of object detection and recognition, image segmentation,   ency for Salient Object Detection,” Appeared in the 13  Inter-  future, in addition to a key component of industrial auto-  on enclosed ellipsoid,”Robotica, vol.19,  pp.381-394,2001.
 and scene understanding. To tackle the high complexity   national Conference on Computer Vision, Barcelona, Spain,   mation. To achieve autonomy with desired behaviors, a lot   2.  Lin WS, Tsai CH and Liu JS, 2001, “Robust neuro-fuzzy con-
 November 2011. (ICCV-2011)

 and  to  design  e cient  algorithms  for  practical  applica-  of issues including sensing, planning and control are go-  trol of multivariable ystems by tuning consequent member-

 tions, my approaches toward solving the aforementioned   2.  Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh. “Multiple   ing on to be resolved to a satisfactory level ensuring the   ship functions,” Fuzzy Sets and Systems, vol.124, pp.181-195,
 vision problems rely extensively on the use of machine   Kernel Learning for Dimensionality Reduction,” Appeared in   safety and comfort of human-robot interaction and meet-  2001.
 IEEE Transactions on Pattern Analysis and Machine Intelli-
 learning techniques.   gence, vol. 33, no. 6, pp. 1147-1160, 2011. (TPAMI)  ing the needs of human beings.   3.  Lai HC, Liu JS, Lee DT, Wang LS, 2003, “Design parameters
                                                                   study on the stability and perception of riding comfort of the

 In our recent research e orts, my students and I have been   3.  Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh. “Dimen-  Along the line of robotics research, at present we conduct   electrical  motorcycles under rider leaning,”  Mechatronics,
 working on exploring the visual saliency information for   sionality Reduction for Data in Multiple Feature Representa-  both simulation and experimental research in the follow-  vol.13, pp.49-76, 2003.
 more satisfactorily addressing a number of vision tasks.   tions,” Advances in Neural Information Processing Systems   ing topics:  4.  Liang TC, Liu JS, Hung G.-T. and Chang Y.-Z., 2005, “Practi-

 The  rst problem we investigate is the image co-segmen-  21, edited by D. Koller, Y. Bengio, D. Schuurmans, L. Bot-  1.  Control and Optimization: (act while optimizing) to   cal and flexible path planning for car-like mobile robot using

 tation. By constructing a prior term in the energy function   tou, and A. Culotta, pp. 961-968, MIT Press, Cambridge MA,   carry out the task such as pursuit-evasion.  maximal-curvature cubic spirals,” Robotics and Autonomous
 2009. (NIPS-2008)
 to account for the co-saliency over a set of images and a         Systems, vol.52, no.4, pp.312-335, 2005.
 global tem to respect the submodular regularity, we have   4.  Yen-Yu Lin,  Tyng-Luh Liu, and Chiou-Shann Fuh. “Local   2.  Smooth path planning: develop soft computing based   5.  Pan WH, Liu JS, Ku WY, 2007, “Fast collision detection for the


 established a new and e cient formulation of energy   Ensemble Kernel Learning for Object Category Recognition,”   (e.g. parallel genetic algorithm based) approaches to   scaled convex polyhedral objects with relative  motion,”IEEE
 Appeared in IEEE Computer Society International  Confer-
 minimization that leads to state-of-the-art performances   ence on Computer Vision and Pattern Recognition, Minneapo-  account  for  physical  characteristics  of  mobile  robot   Int. Symposium on Assembly and Manufacturing, Ann Arbor,
 for image co-segmentation. Building on this success, we   lis, MN, USA, June 2007. (CVPR-2007, Oral)   motion.  Michigan, July, 2007.
 are motivated to consider the problem of salient object   5.  Hwann-Tzong Chen, Tyng-Luh Liu, and Chiou-Shann Fuh.   3.  3D SLAM (simultaneous localization and map build-  6.  Wei JH, Liu JS, 2009, “Mobile Robot Path Planning with eta3
 detection. Conceptually, our method can be best under-  “Tone Reproduction: A Perspective from Luminance-Driven   ing) in indoor environment and its applications in mo-  splines Using Spatial-Fitness-Sharing Variable-length Genetic
 stood through an integration of two graphical models that   Perceptual Grouping,” International Journal of Computer Vi-  bile robot navigation.  Algorithm,”  2009  IEEE International  Conference on Intel-
                                                                   ligent Robots and Systems, St. Louis, Missouri, USA, Oct.,
 correspond to explaining the objectness and saliency esti-  sion,  vol. 65, no. 1-2, pp. 73-96, November 2005. (IJCV)  2009.
 mations, respectively. Via carrying out a two-step optimi-  6.  Yen-Yu Lin,  Tyng-Luh  Liu,  and  Hwann-Tzong  Chen.  “Se-  4.  Robot-assisted wireless sensor network: data collec-
 zation procedure, the proposed technique can iteratively   mantic Manifold Learning for Image Retrieval,” Proceedings   tion navigation and path planning, localization.  7.  Ho YJ, Liu JS, 2010,” Simulated annealing based algorithm
                                                                   for smooth robot path planning with different kinematic con-
 th
 improve the quality of the estimations of objectness and   of the 13  Annual ACM International Conference on Multi-  straints,” 25  ACM Symposium on Applied Computing, Sierre,
                                                                            th
 saliency for a scene, and yields the results of salient object   media, pp. 249-258, Singapore, November 2005 (ACM MM-  Switzerland, March, 2010.
 detection. Our ongoing research along this line is to use   2005, Best Student Papers Session)  8.  YS Chou and JS Liu 2011, Indoor 3D Map Building by Laser
 the salience cue to improve solving the task of object rec-  7.  Hwann-Tzong Chen, Huang-Wei Chang, and Tyng-Luh Liu.   Range Finder Using a Four-bar Linkage Rotating Motion Plat-
 ognition.  “Local Discriminant Embedding and Its Variants,” Appeared   form, 11  International Conference on Automation Technol-
                                                                          th
 in IEEE Computer Society International Conference on Com-         ogy, Daoliu, Yunlin, Taiwan, Nov. 2011.
 The other main research activities in my lab are to develop   puter Vision and Pattern Recognition, vol. 2, pp. 846-853, San

 speci c computer vision techniques and exploit new vi-  Diego, CA, USA, June 2005. (CVPR-2005, Oral)  9.  KM Chiu and JS Liu 2012, Path planning of a data mule for
 sion applications in all aspects for the next-generation im-  8.  Tyng-Luh Liu and Hwann-Tzong Chen. “Real-Time Tracking   data collection in the sensor network by using an improved
                                                                   clustering-based genetic algorithm, 2012 International Con-
 aging/video devices such as Kinect and 3-D cameras.  Using Trust-Region Methods,” IEEE Transactions on Pattern   ference on  Affective Computing and Intelligent  Interaction
 Analysis and Machine Intelligence, vol. 26, no. 3, pp. 397-
                                                                   (ICACII 2012), Taipei, Taiwan, Feb. 2012.
 402, March 2004. (TPAMI)
                                                                10.  YC Lin, JS Liu, KM Chiu, 2012, A novel hybrid localization
                                                                   system combining a hexagon-based  algorithm  and  mobile
                                                                   anchor,” IET International Conference on Automatic Control
                                                                   and Artifi cial Intelligence, Xiamen, China, Mar. 2012
 研究人員
 86  Research Faculty
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