Keynote Speakers

                                                                                                               Keynote Speech 1
Speaker: Prof Gaurav Sharma (U. Rochester)
Title: Visual Data Analytics for Wide Area Motion Imagery
Abstract

The widespread availability of high resolution aerial imagery covering wide geographical areas is spurring a revolution in large scale visual data analytics. Specifically, modern aerial wide area motion imagery (WAMI) platforms capture large high resolution at rates of 1-3 frames per second. The sequences of images, which individually span several square miles of ground area, represent rich spatio-termporal datasets that are key enablers for new applications. The effectiveness of such analytics can be enhanced by combining WAMI with alternative sources of rich geo-spatial information such as road maps or prior georegistered images. We present results from our recent research in this area covering three topics. First, we describe a novel method for pixel accurate, real-time registration of vector roadmaps to WAMI imagery based on moving vehicles in the scene. Next, we present a framework for tracking WAMI vehicles across multiple frames by using the registered roadmap and a new probabilistic framework that allows us to better estimate associations across multiple frames in a computationally tractable algorithm. Finally, in the third part, we highlight, how we can combine structure from motion and our proposed registration approach to obtain 3D georegistration for use in application such as change detection. We present results on multiple WAMI datasets, including nighttime infrared WAMI imagery, highlighting the effectiveness of the proposed methods through both visual and numerical comparisons.

Speaker Biography

Gaurav Sharma is a professor in the Departments of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology, and a Distinguished Researcher in Center of Excellence in Data Science (CoE) at the Goergen Institute for Data Science at the University of Rochester. He received the PhD degree in Electrical and Computer engineering from North Carolina State University, Raleigh in 1996. From 1993 through 2003, he was with the Xerox Innovation group in Webster, NY, most recently in the position of Principal Scientist and Project Leader. His research interests include data analytics, cyber physical systems, signal and image processing, computer vision, and media security; areas in which he has 55 patents and has authored over 225 journal and conference publications. He served as the Editor-in-Chief for the IEEE Transactions on Image Processing from 2018 through 2020, and for the Journal of Electronic Imaging from 2011 through 2015. He is a member of the IEEE Publications, Products, and Services Board (PSPB), the IEEE Signal Processing Society Board of Governwors, and chaired the IEEE Conference Publications Committee in 2017-18. He is the editor of the Digital Color Imaging Handbook published by CRC press in 2003. Dr. Sharma is a fellow of the IEEE, a fellow of SPIE, a fellow of the Society for Imaging Science and Technology (IS&T) and has been elected to Sigma Xi, Phi Kappa Phi, and Pi Mu Epsilon. In recognition of his research contributions, he received an IEEE Region I technical innovation award in 2008 and the IS&T Bowman award in 2021. Dr. Sharma served as a 2020-2021 Distinguished Lecturer for the IEEE Signal Processing Society.

                                                                                                               Keynote Speech 2
Speaker: Prof Habib Zaidi (Geneva University Hospital)
Title: Deep learning-powered multimodality medical image analysis
Abstract: Positron emission tomography (PET), x-ray computed tomography (CT) and magnetic resonance imaging (MRI) and their combinations (PET/CT and PET/MRI) provide powerful multimodality techniques for in vivo imaging. Tis talk presents the fundamental principles of multimodality imaging and reviews the major applications of artificial intelligence (AI), in particular deep learning approaches, in multimodality medical image analysis. It will inform the audience about a series of advanced development recently carried out at the PET instrumentation & Neuroimaging Lab of Geneva University Hospital and other active research groups. To this end, the applications of deep learning in five generic fields of multimodality medical imaging, including imaging instrumentation design, image denoising (low-dose imaging), image reconstruction quantification and segmentation, radiation dosimetry and computer-aided diagnosis and outcome prediction are discussed. Deep learning algorithms have been widely utilized in various medical image analysis problems owing to the promising results achieved in image reconstruction, segmentation, regression, denoising (low-dose scanning) and radiomics analysis. This talk reflects the tremendous increase in interest in quantitative molecular imaging using deep learning techniques in the past decade to improve image quality and to obtain quantitatively accurate data from dedicated standalone (CT, MRI, SPECT, PET) and combined PET/CT and PET/MRI imaging systems. The deployment of DL-powered methods when exposed to a different test dataset requires ensuring that the developed model has sufficient generalizability. This is an important part of quality control measures prior to implementation in the clinic. Novel deep learning techniques are revolutionizing clinical practice and are now offering unique capabilities to the clinical medical imaging community. Future opportunities and the challenges facing the adoption of deep learning approaches and their role in molecular imaging research are also addressed.
Speaker Biography

Habib Zaidi is Chief physicist and head of the PET Instrumentation & Neuroimaging Laboratory at Geneva University Hospital and full Professor at the medical school of the University of Geneva. He is also a Professor at the University of Groningen (Netherlands), the University of Southern Denmark (Denmark) and Óbuda University (Hungary). His research is supported by the Swiss National Foundation, the European Commission, private foundations and industry (Total 10M+ US$) and centres on hybrid imaging instrumentation (PET/CT and PET/MRI), computational modelling and radiation dosimetry and deep learning. He was guest editor for 14 special issues of peer-reviewed journals and serves and serves as founding Editor-in-Chief (scientific) of the British Journal of Radiology (BJR)|Open, Deputy Editor for Medical Physics and is on the editorial board of leading journals in medical physics and medical imaging. He has been elevated to the grade of fellow of the IEEE, AIMBE, AAPM, IOMP, AAIA and the BIR. His academic accomplishments in the area of quantitative PET imaging have been well recognized by his peers since he is a recipient of many awards and distinctions among which the prestigious ($100,000) 2010 Kuwait Prize of Applied Sciences (known as the Middle Eastern Nobel Prize). Prof. Zaidi has been an invited speaker of over 160 keynote lectures and talks at an International level, has authored over 400+ peer-reviewed articles (h-index=76, 21,500+ citations) in prominent journals and is the editor of four textbooks.

                                                                                              Keynote Speech 3
Speaker: Prof Michael Milford (QUT)
Title: Trusted, privacy compliant and introspective positioning systems
Abstract: The positioning field has huge opportunities to develop a more integrated approach to positioning technology, for example through a more organic integration of both GNSS type approaches and the rapid advances in robotics-, AI- and computer-vision driven positioning capabilities. There are also still untapped sources of inspiration for further innovation in positioning technology such as the natural world. The positioning community shares the challenges and opportunities faced by other fields like AI, in developing technology that is acceptable for widespread usage by society that adheres to our expectations and requirements around performance, introspective capability and privacy compliance. In this talk I’ll highlight some of the key areas in which we’re pursuing these goals, highlighting projects ranging from positioning technology for autonomous vehicles to the creation of new ubiquitous positioning services. I’ll touch on some of the key insights we’ve learnt on this journey and highlight exciting areas for future innovation and collaboration between disciplines and sectors.
Speaker Biography

Professor Milford conducts interdisciplinary research at the boundary between robotics, neuroscience, computer vision and machine learning, and is a multi-award winning educational entrepreneur. His research models the neural mechanisms in the brain underlying tasks like navigation and perception to develop new technologies in challenging application domains such as all-weather, anytime positioning for autonomous vehicles. From 2022 – 2027 he is leading a large research team combining bio-inspired and computer science-based approaches to provide a ubiquitous alternative to GPS that does not rely on satellites. He is also one of Australia’s most in demand experts in technologies including self-driving cars, robotics and artificial intelligence, and is a passionate science communicator. He currently holds the position of Director of the QUT Centre for Robotics, Australian Research Council Laureate Fellow, Professor at the Queensland University of Technology, and is a Microsoft Research Faculty Fellow and Fellow of the Australian Academy of Technology and Engineering.

                                                                                                            Keynote Speech 4
Speaker: Prof Svetha Venkatesh (Deakin University)
Title: TBD.
Abstract: TBD.
Speaker Biography

Svetha Venkatesh is an Alfred Deakin Professor and a co- Director of Applied Artificial Intelligence Institute (A2I2) at Deakin University. She was elected a Fellow of the International Association of Pattern Recognition in 2004 for contributions to formulation and extraction of semantics in multimedia data, a Fellow of the Australian Academy of Technological Sciences and Engineering in 2006, and a Fellow of the Australian Academy of Science in 2021 for ground-breaking research and contributions that have had clear impact. In 2017, Professor Venkatesh was appointed an Australian Laureate Fellow, the highest individual award the Australian Research Council can bestow. Professor Venkatesh and her team have tackled a wide range of problems of societal significance, including the critical areas of autism, security and aged care. The outcomes have impacted the community and evolved into publications, patents, tools and spin-off companies. This includes 690+ publications, three full patents, one start-up company (icetana) and two significant products (TOBY Playpad, Virtual Observer). Professor Venkatesh has tackled complex pattern recognition tasks by drawing inspiration and models from widely diverse disciplines, integrating them into rigorous computational models and innovative algorithms. Her main contributions have been in the development of theoretical frameworks and novel applications for analysing large scale, multimedia data. This includes development of several Bayesian parametric and non-parametric models, solving fundamental problems in processing multiple channel, multi-modal temporal and spatial data.

                           Keynote Speech 5
Speaker: Prof David Suter (Edith Cowan University)
Title: TBD.
Abstract: TBD.
Speaker Biography

David Suter is a Professor of Computer Science in the School of Science (Computing and Security). He leads a team carrying out leading research in computer vision and big-data analysis. His special expertise includes robust statistical fitting, computational geometry and machine learning.