Keynote Speakers of CGIP 2024
Prof. Kazunori Miyata
Japan Advanced Institute of Science and Technology (JAIST),
Japan
Prof. Kazunori Miyata received his B.S. in Applied Physics from Tohoku University in 1984 and his Master of Engineering and Doctor of Engineering from Tokyo Institute of Technology in 1986 and 1997, respectively. He has been a professor at Japan Advanced Institute of Science and Technology (JAIST) since 2002. In 1986, he became a researcher at the Tokyo Research Laboratory of IBM Japan, and in 1998 he was an assistant professor at the Faculty of Arts, Tokyo Polytechnic University. Since 2021, he is an academic staff under the joint appointment academic scheme at Universiti Utara Malaysia School of Creative Industry Management & Performing Arts. He is a fellow of The Institute of Image Electronics Engineers of Japan. His research mainly focuses on computer graphics, human computer interaction, fun computing, XR, and multimedia applications. He is a past president of the Japan Society for Art Science and Technology. He is a member of The Virtual Reality Society of Japan, Information Processing Society of Japan, Japan Society of Art Science and Technology, ACM, and IEEE.
Speech Title: "Computer Graphics as a Tool to Support Creative Activity"
Abstract: Thanks to the benefits of deep learning technologies, it is now possible to create highly accurate visual digital content with little effort. This technology, generative AI, is expected to drastically reduce our intellectual workload and dramatically change the social structure. On the other hand, CG technology is also a core technology that has already penetrated deeply into our daily lives. However, it is clear that the technical hurdles and workloads, such as the modeling capability of a target object, are high to realize one's idea as a CG image. In response to this challenge, a method called procedural technology is useful as a tool to streamline production work, i.e. a rapid prototyping tool, because it can instantly generate the desired content simply by changing parameters. In general, the design process includes reflection on the outputs. Designers improve their work through introspection and quiet thinking while working. Is it really possible for current generative AI to encourage such introspection? This talk will position CG as a tool to support creative activities and describe a framework for mining users' creativity through several examples. It will also show the potential for sharing content design ideas with others and for inheriting the design process.
Prof. Kuo-Sheng Cheng
National Cheng Kung University, Taiwan
Prof. Kuo-Sheng Cheng received his BSc. and M.Sc. degrees both in electrical engineering in 1980 and 1982 from the National Cheng Kung University, Tainan, Taiwan, and the M.S. degree in biomedical engineering in 1988 from Rensselaer Polytechnic Institute, Troy, NY, USA. In 1990, he received a Ph.D. degree in electrical engineering from the National Cheng Kung University. Currently, he is a Professor at the Department of Biomedical Engineering and an Adjunct Professor at the Institute of Oral Medicine at National Cheng Kung University. He also serves as the Director of the Department of Medical Engineering at National Cheng Kung University Hospital and the Director of the Engineering and Technology Promotion Center financially supported by the National Science and Technology Council, TAIWAN. He is also the Chair of the IEEE EMBS Tainan Chapter. His research interests include medical image processing and analysis, electrical impedance tomography system development and applications, and biomedical instrumentation and measurement.
Speech Title: "The Applications of Deep Learning in Separation of Heart and Lung Impedance Images in Electrical Impedance Tomography"
Abstract: Electrical impedance tomography (EIT) is a noninvasive medical imaging technique that measures the impedance of tissues by applying low-amplitude electrical currents and measuring the resulting voltages. The impedance distribution inside the body can then be reconstructed by solving an inverse problem, which provides useful information for diagnosing and monitoring a variety of conditions, such as respiratory and circulatory functions. One of the challenges of EIT in chest applications is separating the impedance signals from the heart and lungs. This is difficult because the two organs are close together and their impedance signals are mixed. However, deep learning has emerged as a promising approach to solving this problem. Deep learning models can be trained on EIT data to learn the features that distinguish between heart and lung impedance. Once trained, these models can be used to separate the two signals and produce more accurate EIT images. In our recent study, we proposed a novel semi-Siamese U-Net architecture for heart and lung impedance image separation. This architecture is based on the state-of-the-art U-Net, with modifications and extensions to improve performance on EIT image separation.