2024 International Conference on Computer Vision, Robotics and Automation Engineering(CRAE 2024)
Speakers
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Speakers


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Prof. Zhi Gao

Wuhan University, China

Biography:

Prof. Zhi Gao is currently a professor and vice dean of the School of Remote Sensing and Information Engineering at Wuhan University, China. He currently also serves as the vice director of the working committee of the Hubei Luojia Laboratory, China. He received the prestigious “national plan for young talents” award and Hubei Province Funds for Distinguished Young Scientists. In addition, he is also a “Chutian Scholar” distinguished Professor in Hubei Province. He has been the PI/Co-PI for more than 10 projects supported by the National Natural Science Foundation of China, the Ministry of Education of China, and the Hubei Provincial Science and Technology Agency. His research interests include computer vision, machine learning, and remote sensing. In particular, he has a strong interest in intelligent systems relevant to vision problems and applications. He has authored/co-authored more than 100 journal and conference articles such as the IJCV, IEEE T-PAMI, IEEE T-IP, IEEE T-RO, IEEE T-ITS, CVPR, ECCV, IROS, ICRA.


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Prof. Ljiljana Trajkovic

IEEE Fellow

Simon Fraser University, Canada



Title: Data Mining and Machine Learning for Analysis of Network Traffic

 

Abstract:

Collection and analysis of data from deployed networks is essential for understanding modern communication networks. Data mining and statistical analysis of network data are often employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic while various machine learning techniques proved valuable for predicting anomalous traffic behavior. In described case studies, traffic traces collected from various deployed networks and the Internet are used to characterize and model network traffic, analyze Internet topologies, and classify network anomalies.


Biography:

Ljiljana Trajkovic received the Dipl. Ing. degree from the University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from the University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. She served as IEEE Division X Delegate/Director, President of the IEEE Systems, Man, and Cybernetics Society, and President of the IEEE Circuits and Systems Society. Dr. Trajkovic serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems and Associate Editor-in-Chief of the IEEE Open Journal of Systems Engineering. She is a Distinguished Lecturer of the IEEE Circuits and System Society, a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society, and a Fellow of the IEEE.


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Prof. Zhao Zhang
IEEE Senior Member

Hefei University of Technology, China



Title: Deep Visual Computing for Degraded Image Restoration and Enhancement


Abstract:

Due to the hardware limitations of image acquisition device, improper acquisition operations or bad weathers, the images captured in open world usually contain different degradation information, such as noise, rain, haze, low light and blur. While the degradation will directly reduce the visual perception effect, which will also seriously affect the subsequent high-level visual tasks. As such, it is of great significance to recover the lost information in the process of image degradation by using low-level visual processing methods, which can not only improve the visual perception effect, but also enhance the understanding and recognition of images in subsequent high-level tasks. Besides, most of existing models are driven by manually labeled paired data, while synthetic data usually have a large distribution difference from real data, which will lead to limited generalization ability of the model. In this talk, I will also introduce the recent advances on the data-driven deep degraded image restoration methods that were recently developed by the speaker's group. By the powerful fitting ability of deep neural networks, the proposed methods can model the degraded information more accurately and obtain better image restoration performance. 


Biography:

Dr. Zhao Zhang is a Full Professor at the School of Computer and Information, Hefei University of Technology, Hefei, China. I received a PhD degree from the City University of Hong Kong, supervised by Prof. Tommy W.S. Chow (IEEE Fellow), in 2013. During my PhD study, I visited the National University of Singapore, working with Prof. Shuicheng Yan (ACM/AAAI/IEEE/SAEng/ IAPR Fellow), from Feb to May 2012. I also visited the Chinese Academy of Sciences, working with Prof. Cheng-Lin Liu (IEEE/IAPR Fellow), from Sep to Dec 2012. My research interests include Machine Learning, Computer Vision, and Pattern Recognition. I have authored/co-authored over 130 technical papers published at prestigious journals and conferences, including 48 IJCV or IEEE/ACM Transactions papers (e.g., IEEE TIP, IEEE TKDE, IEEE TNNLS, IEEE TCYB, IEEE TSP, IEEE TCSVT, IEEE TMM), and 30 Top-tier conference papers (e.g., CVPR, NeurIPS, ACM MM, ICLR, AAAI and IJCAI), with Google Scholar citations over 5,900 times and H-index 44. I am serving/served as an Associate Editor (AE) of IEEE Transactions on Image Processing (IEEE TIP), Pattern Recognition (PR), and Neural Networks (NN). Besides, I have been serving as a SPC member/Area Chair of ACM MM, AAAI, IJCAI, SDM, and BMVC. I am now a Senior Member of the IEEE and CCF. Personal homepage:https://sites.google.com/site/cszzhang.


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Prof. Gyu Myoung Lee

IEEE Senior Member

Liverpool John Moores University, UK, and KAIST, Korea


Title: Towards AI powered Trustworthy Internet of Value


Abstract:

Artificial Intelligence (AI) and Internet of Things (IoT) are very important technologies for the future, and recently there has been a lot of research activity to combine AI and IoT, called AIoT (Artificial Intelligence of Things). In addition, data is becoming essential to support AI-based solutions with human interactions. Blockchain is revolutionizing the way transactions are recorded as a machine to create trust. In this context, this talk will introduce key concepts, features and characteristics of the Decentralized Internet (i.e., Web 3.0 and its vision as the Internet of Value), taking into account emerging ICTs integrating AIoT and Blockchain, and related EU projects such as GAIA-X. From the research on decentralized Internet research for Web 3.0, many researchers have recognized that there are security, privacy and trust concerns to realize a user-centric approach for decentralization. To cope with the negative effects of the decentralized Internet, it's necessary to build a trustworthy infrastructure with AI for the future digital economy toward the Internet of Value. Therefore, starting from the new economic paradigm for cyberspace, data ecosystem and its characteristics, this talk will present future directions for realizing decentralized Internet with AI-powered trust technology.


Biography:
Gyu Myoung Lee is a professor at Liverpool John Moores University (LJMU), UK. He is also affiliated with KAIST, Daejeon, Rep. of Korea, as an Adjunct Professor since 2012. Before joining the LJMU in 2014, he worked at the Institut Mines-Telecom since 2008. In 2012, he was invited to work at ETRI, Rep. of Korea. He worked as a research professor at KAIST, Rep. of Korea, and as a guest researcher at NIST, USA, in 2007. 

His research interests include the Internet of Things, digital twin, computational trust, blockchain with privacy preservation, data and AI governance, knowledge centric networking and services considering all vertical services, Smart Grid, energy saving networks, cloud-based big data analytics platform, and multimedia networking and services.

Prof. Lee has been actively participating in standardization meetings including ITU-T SG 13 and SG20, IETF and oneM2M, etc., and currently serves as a Working Party chair and the Rapporteur of Q16/13 on trustworthy networking and services and Q4/20 on data analytics, sharing, processing and management in ITU-T. He is the Vice-Chair of ITU-T FG-AN and FG-AI4A as well as the Convenor of CG-AIoT and Web3-adhoc. He was also the chair of the ITU-T Focus Group on Data Processing and Management (FG-DPM). He has contributed more than 500 proposals for standards and published more than 200 papers in academic journals and conferences. He received several Best Paper Awards in international and domestic conferences and served as a reviewer of IEEE journals/conference papers and an organizer/member of the committee of international conferences. He is a Senior Member of IEEE.

Prof. Lee received his BS degree in electronic and electrical engineering from Hong Ik University, Seoul, Rep. of Korea, in 1999 and received his MS and PhD. degree from KAIST, Daejeon, Rep. of Korea, in 2000 and 2007, respectively.



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Prof. Xianghua Xie

Swansea University, UK


Title: Robotics and Automation from a Computer Vision Perspective


Biography:

Xianghua Xie (XX) is a professor of computer science at Swansea University, where he leads the Computer Vision and Machine Learning group (http://csvision.swan.ac.uk) and the Intelligent Robotics research group. He was a recipient of an RCUK academic fellowship and has been an investigator on several projects funded by UKRI, Leverhulme, NISCHR, etc. XX has made notable contributions in the areas of Pattern Recognition and Machine Intelligence and their applications to real-world problems. Those of significant importance include federated learning, detecting abnormal patterns in complex visual and medical data, assisted diagnosis using automated image analysis, fully automated volumetric image segmentation, registration, and motion analysis, machine understanding of human action, efficient deep learning, and deep learning on irregular domains. He has published around 200 papers and (co-)edited several proceedings.

 

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Assoc. Prof. Taogang Hou

Beijing Jiaotong University, China


Biography:
Taogang Hou received the B.E. degree and Ph.D. degree in mechanical engineering from Beihang University, Beijing, China, in 2016 and 2020. He is currently an associate professor at the School of Electronic and Information Engineering, Beijing Jiaotong University, China. He has been selected to join the “Youth Talent Support Project” of the Chinese Association for Science and Technology. His research interests include intelligent robotics, visual perception under high-speed movement, and smart transportation systems. He is now a member of the Intelligent Robot Technical Committee of the China Computer Federation (CCF), a member of the Professional Committee of Cognitive Systems and Information Processing of the Chinese Association for Artificial Intelligence (CAAI), and a member of the Medical-Industrial Integration Professional Committee of the China Health Culture Association (CHCA). He has presided over several projects supported by the National Natural Science Foundation of China, Beijing Natural Science Foundation, and China Postdoctoral Science Foundation. He has published more than 20 academic papers in journals such as Mech. Mach. Theory and IEEE International Conference on Robotics (ICRA). He has also guided students to win the Gold Prize in the “Challenge Cup” National College Student Entrepreneurship Competition and the First Prize in the "Challenge Cup" National Undergraduate Extracurricular Academic Science and Technology Contest.