2023 2nd International Conference on Optics and Machine Vision


Keynote Speakers



Prof. Bin Liu, Dalian University of Technology, China


Title: Personalized Computer-Assisted Medicine Based on Graphics and Image Processing

Abstract: The human pursuit of health is endless. Nowadays, countries all over the world have increased their investment in the field of health care. As an emerging discipline in the future, computer-assisted medical technology has attracted much attention. This is a deeply intersecting discipline. It not only includes computer technology and medical technology, but also requires the intervention of artificial intelligence technology, mechanical engineering, mechanics and other disciplines. In the past ten years, our research group has done some exploratory research on computer-assisted personalized orthopedic surgery (especially personalized orthopedic prosthesis modeling). In addition, we have also conducted some more challenging research in intelligent image processing and analysis. In future research, it is hoped that more disciplines can work together to truly solve clinical practical difficulties.

Experience: Bin Liu, Professor, PhD supervisor, Head of Digital Media Technology Department, Director and Technical Leader of Liaoning Provincial Key Laboratory of Medical Simulation Technology, High-end talent in Dalian, member of GF project evaluation expert group, CCF member of Chinese Computer Society, ACM member of American Computer Society, MICCAI member. He received his B.E. degree, M.S. degree and Ph.D. degree from Dalian University of Technology in 2000 to 2009, and he conducted a joint research as a visiting scholar at National University of Singapore in 2018-2019. His main research interests are computer vision and graphic images, medical image processing and 3D reconstruction, computer-aided preoperative planning and simulation.


Prof. Chenqiang Gao, Chongqing University of Posts and Telecommunications, China



Title: Infrared small target detection: From model-driven methods to data-driven methods

Abstract: Infrared small target detection is one of the key techniques in infrared search and track systems. However, the small-dim appearance of the small target and the class imbalance between the target and the background make accurate small target detection very difficult. This talk will firstly report how our model-driven works (IPI model and MRF guided MoG noise model) address this problem. Then, I will report our recent data-driven based infrared small target detection approaches. We designed a convolutional neural network to fully explore the global and local characteristics of the infrared small target image. Besides, we also proposed a transformer-based architecture to explore the long-term dependence within the infrared small target image. Finally, the future trends and remaining challenges in this field will be discussed.


Experience: Chenqiang Gao received the B.S. degree in computer science from China University of Geosciences, Wuhan, China, in 2004 and the Ph.D. degree in Control Science and Engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2009. In August 2009, he joined School of Communications and Information Engineering at Chongqing University of Posts and Telecommunications (CQUPT), Chongqing, China. In September 2012, he joined the Informedia Group in School of Computer Science at Carnegie Mellon University (CMU), working on Multimedia Event Detection (MED) and Surveillance Event Detection (SED) as a visiting scholar. In April 2013, he became a PostDoctoral Fellow and continued work on MED and SED until March 2014 when he returned to CQUPT. He is currently a Professor at CQUPT. His research interests include infrared image analysis, video analysis, machine learning. He has published approximately 80 technical articles in refereed journals and proceedings, including TIP, TMM, TGRS, PR, and CVPR, ECCV, AAAi, ACM MM, etc.