Computational Symmetry

日時:
2007年11月22日 15時00分～17時00分

場所:
京都大学大学情報メディアセンター南館 202講義室

講師:
Dr. Yanxi Liu氏

講師所属:
Associate Professor of Computer Science and Engineering and Electrical Engineering,

講師略歴:
Dr. Yanxi Liu received her B.S. degree in physics/electrical engineering in Beijing and her Ph.D. degree in computer science for group theory applications in robotics from University of Massachusetts. Her postdoctoral training was performed at LIFIA/IMAG, Grenoble, France. She also spent one year at DIMACS (NSF center for Discrete Mathematics and Theoretical Computer Science) with an NSF research-education fellowship award. Currently, Dr. Liu is with the faculty of the Computer Science Engineering and Electrical Engineering departments of Penn State University. She is also an adjunct faculty in the Robotics Institute (RI) and the Machine Learning department of Carnegie Mellon University, the Radiology Department of University of Pittsburgh, and a guest professor of Computer Science Department, Huazhong University of Science and Technology in China. Dr. Liu's research applications and publications span a wide range of areas in computer vision, computer graphics, robotics and computer aided diagnosis in medicine, with two central themes: computational symmetry and discriminative subspace learning. With her colleagues, Dr. Liu won the first place in the clinical science category and the best paper overall at the Annual Conference of Plastic and Reconstructive Surgeons for the paper "Measurement of Asymmetry in Persons with Facial Paralysis." Dr. Liu chaired the First International Workshop on Computer Vision for Biomedical Image Applications (CVBIA) in conjunction with ICCV 2005 and co-edited the book: "CVBIA: Current Techniques and Future Trends" (Springer-Verlag LNCS). Dr. Liu serves as a reviewer/committee member/panelist for all major journals, conferences as well as NIH/NSF panels on computer vision, pattern recognition, biomedical image analysis, and machine learning. She had been a chartered study section member for Biomedical Computing and Health Informatics at NIH. She is a senior member of IEEE and the IEEE Computer Society.

講演概要:
Symmetry is an essential mathematical concept, as well as a ubiquitous, observable phenomenon in nature, science and art. Symmetry implies a potential structural efficiency gain that makes it universally appealing, especially to computational science. Much of our understanding and appreciation of the world is based on the perception and recognition of shared or repeated patterns. Recognition and categorization of symmetry and regularity is the first step towards capturing the essential elements of a problem, while at the same time minimizing computational redundancy. Our research in the realm of "Computational Symmetry" explores the use of symmetry groups and their statistical deviations in a wide range of applications in computer vision, computer graphics, robotics and medical image analysis, including texture regularity discovery, texture analysis, synthesis, tracking and manipulation, human gait recognition, human identification, expression classification, robotics assembly planning, computer aided diagnosis of degenerative neurological disorders from structural MR images, and qualitative and quantitative evaluation of the firing fields of grid cells in rat brains. The central theme of this talk exposes an important emerging research area, and the promise and perils of making the mathematical concept of symmetry group theory computationally feasible for real world problems.