Professor Yen-Wei Chen
Ritsumeikan University, Japan
Yen-Wei Chen received the B.E. degree in 1985 from Kobe Univ., Kobe, Japan, the M.E. degree in 1987, and the D.E. degree in 1990, both from Osaka Univ., Osaka, Japan. He was a research fellow with the Institute for Laser Technology, Osaka, from 1991 to 1994. From Oct. 1994 to Mar. 2004, he was an associate Professor and a professor with the Department of Electrical and Electronic Engineering, Univ. of the Ryukyus, Okinawa, Japan. He is currently a professor with the college of Information Science and Engineering, Ritsumeikan University, Japan. He is also a visiting professor with the College of Computer Science, Zhejiang University, China. He was a visiting professor with the Oxford University, Oxford, UK in 2003 and a visiting professor with Pennsylvania State University, USA in 2010. His research interests include medical image analysis, computer vision and computational intelligence. He has published more than 300 research papers in a number of leading journals and leading conferences including IEEE Trans. Image Processing, IEEE Trans. SMC, Pattern Recognition. He has received many distinguished awards including ICPR2012 Best Scientific Paper Award, 2014 JAMIT Best Paper Award, Outstanding Chinese Oversea Scholar Fund of Chinese Academy of Science. He is/was a leader of numerous national and industrial research projects.
Speech Title: Deep Learning in Medical Imaging
Abstract: Recently, deep learning (DL) plays important roles in many academic and industrial areas especially in computer vision and image recognition. Deep learning uses a neural network with deep structure to build a high-level feature space. It learns data-driven, highly representative, hierarchical image features, which have proven to be superior to conventional hand-crafted low-level features and mid-level features. In ILSVRC2015 (an Annual competition of image classification at large scale), higher recognition accuracy by deep learning than human has been achieved [He et al, 2015]. Deep learning (DL) has also been applied to medical image analysis. Compared with DL-based natural image analysis, there are several challenges in DL-based medical image analysis due to their high dimensionality and limited number of labeled training samples. We proposed several deep learning techniques for medical image analysis including medical image segmentation, medical image detection and medical image recognition. In this keynote talk, I will talk about current progress and futures of medical image analysis with deep learning.
Faculty of Informatics, Kogakuin University, Tokyo, Japan
Yoshifumi Manabe was born in 1960. He received his B.E., M.E., and Dr.E. degrees from Osaka University, Osaka, Japan, in 1983, 1985, and 1993, respectively. From 1985 to 2013, he worked for Nippon Telegraph and Telephone Corporation. From 2001 to 2013, he was a guest associate professor of Graduate School of Informatics, Kyoto University. Since 2013, he has been a professor of the Faculty of Informatics, Kogakuin University, Tokyo, Japan. His research interests include distributed algorithms, cryptography, game theory, and graph theory. Dr. Manabe is a member of ACM, IEEE, IEICE, IPSJ, and JSIAM.
Speech Title: Cryptographic protocols using physical cards
Abstract:Computers are commonly used for encryption and decryption in cryptography. In 1990, a new kind of cryptographic protocol has been proposed in which physical cards are used instead of computers to securely calculate values. They are useful when computes cannot be used. In addition, people who has no knowledge of cryptography can execute and understand the protocols. den Boer first showed a five card protocol to securely calculate logical AND of two inputs. Since then, many protocols have been proposed to calculate logical functions and specific computations such as millionaires' problem, voting, random permutation, grouping and so on. This talk shows several protocols and recent results using private operations. The number of cards used in the protocol is the most important criteria to evaluate card-based cryptographic protocols. Using private operations, the theoretically minimum number of cards is achieved in many problems.
Prof. Yin-Tien Wang
Department of Mechanical Engineering, Tamkang University, Taiwan
Yin-Tien Wang received the M.S. degree from Stevens Institute of Technology in 1988 and Ph.D. degree from University of Pennsylvania in 1992, both in mechanical engineering. He is currently a Professor with the Department of Mechanical and Electro-Mechanical Engineering, Tamkang University, New Taipei City, Taiwan, where he is in charge of Robotics and Machine Vision courses. His current interests include real-time vision localization and mapping research and the transference of this technology to robotic and nonrobotic application domains.
Speech Title: Robot Vision and Its Applications in Industry
Abstract: his study presents a brief introduction to current research trends in robot vision and its applications in industry. Many research topics were investigated in the study. Firstly, mobile robot visual localization and mapping algorithm based on estimation methods was discussed. In the algorithm, the moving object detection was integrated with a pedestrian detector to set the location boundaries of image features belonged to an object. The description of the visual landmarks was modified to ensure the robust representation of a persistent visual map. The estimation methods based on filtering and non-filtering were carried out to validate the performance of the developed systems. Secondly, applications of robot additive manufacturing using vision sensor was presented. The additive manufacturing was used as the build-up process for creating or repairing the metal part of complicated geometry to reduce the cost of material and investment. The feasible process parameters of double-pulse gas metal arc welding were determined for wire-and-arc additive manufacturing. Finally, the applications of robot machining in grinding and finishing were investigated. Experimental results and future work on relevant research were discussed to conclude the study.