Conference Keynotes: Broaden Your Horizons 

KEYNOTE: Neutron Imaging Beyond Traditional Radiography (M1)

8:45
Conference Welcome

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KEYNOTE: Advanced neutron imaging, Markus Strobl, Paul Scherrer Institut (PSI) (Switzerland) [view abstract]

Prof. Dr. Markus Strobl is the leader of the Applied Materials group at the Paul Scherrer Institute (PSI) of Switzerland. The Applied Materials Group (AMG) is a group within the Laboratory for Neutron Scattering and Imaging LNS, in the division research with Neutrons and Muons NUM of PSI. AMG is operating 2 dedicated neutron imaging facilities and the neutron strain scanner (diffractometer) POLDI for users from scientific institutions and industry. AMG also provides complementary x-ray imaging (in-situ bi-modal) and has dedicated beamtimes for imaging studies at the test beamline BOA providing an intense cold polarized neutron beam. Strobl has over 230 publications in the field.

The last decade in neutron imaging saw extensive activity in method development. This was only partially due to novel pulsed neutron sources and corresponding new imaging instruments having been established. Also at the state-of-the-art instruments at continuous sources wavelength resolution, polarization and grating interferometry, to name a few, changed the field of neutron imaging sustainably. Both, these new methodical developments but also increased accuracy and demand for quantification, implied a need for more detailed and concise description of the utilized neutron interaction, in particular with respect to neutron scattering. Thus, finally the scattering aspect in neutron imaging gained significantly in weight, which on the one hand puts additional demands on neutron imaging scientists and on the other hand increases the integration of neutron imaging within the suite of neutron science techniques and their applications at large scale neutron sources. Consequently, the basic principles required to be embraced with the access to novel methods, interactions and information shall be out- and underlined. A number of interactions providing valuable new information in novel techniques are known for causing artifacts in conventional applications. These are more important with respect to the better resolution achieved nowadays and the corresponding higher demands for quantification. For the session: Neutron Imaging Beyond Traditional Radiography.

KEYNOTE: Data & Learning (M1)

8:45
Conference Welcome

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KEYNOTE: Small data, big insights, Raja Bala, Amazon (United States) [view abstract]

Dr. Raja Bala is a principal applied scientist at Amazon. His research interests include computer vision, deep learning, image/video processing, mobile imaging, and color imaging. Bala is an inventor on 180 patents and has authored over 100 publications in the field of digital imaging and computer vision. He is co-editor of IEEE-Wiley book: "Computer Vision and Imaging in Intelligent Transportation Systems" and is the principal liaison for numerous industry-university partnerships. Prior to joining Amazon, Bala was principal scientist, and leader of the Collaborative Visual Computing Group at PARC. Bala is a Fellow of IS&T, and a Senior Member of IEEE.

Deep learning has defined the state of the art for many computer vision tasks, thanks to advances in computing hardware and the availability of large datasets. However, for many practical applications, data acquisition and annotation is a costly and time-consuming task. This is especially true in specialized fields such as medical imaging, fashion, and beauty care, where annotation must be carried out by domain experts. We present several novel approaches to tackle the “Small Data” challenge. This includes i) incorporation of domain knowledge into deep networks via regularizers and priors to reduce training demands; ii) image synthesis exploiting latent structure in deep generative models and adversarial methods to generate hard examples; iii) guided image acquisition to ensure high-quality of data; iv) rapid human-in-loop data annotation with augmented reality.

KEYNOTE: SD&A 1 (M2.1)

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KEYNOTE: The long-awaited arrival of holographic interfaces, Shawn Frayne, Looking Glass Factory (United States) [view abstract]

Inspired by movies in the 80s and 90s, Shawn Frayne has been reaching towards the dream of the hologram for over 20 years. Frayne got his start with a classic laser interference pattern holographic studio he built in high school, followed by training in advanced holographic film techniques at MIT. He has been awarded dozens of patents around the world and serves as co-founder and CEO of Looking Glass Factory, based in Brooklyn and Hong Kong.

Holographic or light field generating devices that could enable groups of people to see and interact with genuinely three-dimensional content have long been held as a “holy grail” by those inventors and engineers that work in the field of perfecting the human-computer interface. Now after decades of work, real-time holographic interfaces are at last commercially available. The driving forces behind the emergence of holographic interfaces, the advantages of these headset-free systems compared to head-mounted displays, and the unique characteristics of the first commercially-viable approaches to come to market will be covered here.

PANEL: Watch What You Eat: Panel on Food/Health from the Perspective of AI and Privacy (M2.2)

KEYNOTE: Computational Imaging using Fourier Ptychography and Phase Retrieval (M3)

3:30COIMG-138
KEYNOTE: Computational phase imaging, Laura Waller, University of California, Berkeley (United States) [view abstract]

Laura Waller leads the Computational Imaging Lab, which develops new methods for optical imaging, with optics and computational algorithms designed jointly. She holds the Ted Van Duzer Endowed Professorship and is a Senior Fellow at the Berkeley Institute of Data Science (BIDS), with affiliations in Bioengineering and Applied Sciences & Technology. Laura was a Postdoctoral Researcher and Lecturer of Physics at Princeton University from 2010-2012 and received BS, MEng and PhD degrees from MIT in 2004, 2005 and 2010, respectively. She is a Moore Foundation Data-Driven Investigator, Bakar fellow, Distinguished Graduate Student Mentoring awardee, NSF CAREER awardee, Chan-Zuckerberg Biohub Investigator, SPIE Early Career Achievement Awardee and Packard Fellow.

Scattering severely limits the visual acuity of an imaging system. This talk discusses how diversity in illumination wavelength can be utilized to circumvent the problem of phase randomization in scattered light fields. Amongst other applications, the introduced method allows for holographic measurements of hidden objects around corners and through scattering media, or for interferometric measurements of macroscopic objects with rough surfaces. This is possible as the technique interrogates the scene at two closely spaced optical wavelengths and computationally assembles a complex “synthetic field” at a “synthetic wave,” which is used for further processing. As the synthetic wavelength is the beat wavelength of the two optical wavelengths, it can be picked orders of magnitudes larger, and the computationally assembled synthetic field becomes immune to the deleterious effect of speckle. During the talk, different flavors of the technique will be introduced, using the examples of our latest experimental results. For the session: Computational Imaging using Fourier Ptychography and Phase Retrieval.

KEYNOTE: High-Performance Imaging (T1)

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KEYNOTE: Reducing the barriers to high performance imaging, Charles A. Bouman, Purdue University (United States) [view abstract]

Prof. Charles A. Bouman received a BSEE from the University of Pennsylvania in 1981 and an MS from the University of California at Berkeley in 1982. From 1982 to 1985, he was a full staff member at MIT Lincoln Laboratory and in 1989 he received a PhD in electrical engineering from Princeton University. He joined the faculty of Purdue University in 1989 where he is currently the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering. Prof. Bouman’s research is in the area of computational imaging and sensing where he is focused on the integration of signal processing, statistical modeling, physics, and computation to solve difficult sensing problems with applications in healthcare, material science, physics, chemistry, commercial and consumer imaging. His research resulted in the first commercial model-based iterative reconstruction (MBIR) system for medical X-ray computed tomography (CT), and he is co-inventor on over 50 issued patents that have been licensed and used in millions of consumer imaging products. Professor Bouman is a member of the National Academy of Inventors, a Fellow of the IEEE, a Fellow and Honorary Member of the society for Imaging Science and Technology (IS&T), a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), and a Fellow of the SPIE professional society. He is the recipient of the 2021 IEEE Signal Processing Society, Claude Shannon-Harry Nyquist Technical Achievement Award, the 2014 Electronic Imaging Scientist of the Year award, and the IS&T’s Raymond C. Bowman Award; and in 2020, his paper on Plug-and-Play Priors won the SIAM Imaging Science Best Paper Prize. He was previously the Editor-in-Chief for the IEEE Transactions on Image Processing; a Distinguished Lecturer for the IEEE Signal Processing Society; and a Vice President of Technical Activities for the IEEE Signal Processing Society, during which time he led the creation of the IEEE Transactions on Computational Imaging. He has been an associate editor for the IEEE Transactions on Image Processing, the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the SIAM Journal on Mathematical Imaging. He has also been a Vice President of Publications and a member of the Board of Directors for the IS&T Society, and he is the founder and Co-Chair of the long running IS&T conference on Computational Imaging.

Over the past thirty years, algorithmic advances have achieved what may have previously seemed impossible. However, there remains a vast reservoir of well-known and very effective algorithms that go unused because of enormous practical barriers. In this talk, we present a number of case studies in which we attempt to transition state-of-the-art computational imaging algorithms to practical use in scientific, industrial, and commercial applications. The three examples we will discuss are scientific CT imaging, medical CT, and digital holographic imaging. While these applications span very different requirements and computing platform solutions, the talk will discuss approaches to solving their following common problems: *Throughput and latency: advanced algorithms are often slow, and even if they are not slow, then people inevitably would like them to be implemented with the least expensive hardware. This means that algorithms must be efficient! *Co-design: The days when algorithms could be designed to minimize the number of multiplies is long gone. Algorithms must be designed or redesigned to jointly optimize algorithmic and hardware performance. *Memory reuse: The modern processor is limited by the time it takes to communicate with registers, cache, memory, and other nodes. This means that memory reuse is always a critical algorithmic design objective. *Parallelization: As the number of registers, cores, and nodes grows exponentially, keeping them all busy requires clever algorithmic design. *Ease-of-use: The fastest and best algorithmic implementation is of no value to practitioners if they don’t know how to use it or to set the parameters!

KEYNOTE: Applications I (T1)

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KEYNOTE: Multi-scale representations for human pose estimation: Advances and applications, Andreas Savakis, Rochester Institute of Technology (United States) [view abstract]

Prof. Andreas Savakis is director of the Center for Human-aware AI (CHAI) and Professor of Computer Engineering at the Rochester Institute of Technology. His primary area of research is computer vision, with secondary interests in computational imaging and image processing. Savakis founded the Vision and Image Processing lab (VIP-lab) at RIT, where he works with students on topics including recognition, tracking, segmentation, pose estimation, facial expression, scene analysis, domain adaptation, and robust learning.

Human pose estimation is a topic of interest for many applications, including human-computer interaction, activity recognition, sports analysis and health monitoring. Pose estimation methods have improved significantly in recent years due to advances in deep learning architectures and multi-scale representations. We present an efficient Waterfall Atrous Spatial Pooling (WASP) architecture for multi-scale feature extraction that is useful for both pose estimation (UniPose, OmniPose, HandyPose) and semantic segmentation (WASPnet, GourmetNet). Our Waterfall architecture leverages the efficiency of progressive filtering in cascade, while maintaining multiscale fields-of-view comparable to spatial pyramid configurations. The waterfall approach is used for pose estimation in an encoder-decoder framework producing state of the art results for single person 2D pose with UniPose and multi-person 2D pose with OmniPose.. We extend our framework to other types of pose, such as 3D pose from a single image with UniPose+, 2D hand pose with HandyPose and vehicle pose with VehiPose. We conclude by outlining new diretions and applications for human pose and object pose.

KEYNOTE: Perceptual Video Quality 1 (T1)

This session is jointly sponsored by: Human Vision and Electronic Imaging 2023, and Image Quality and System Performance XX.

Joint Conference Welcome

HVEI-258
KEYNOTE: Bringing joy to Netflix members through perceptual encoding optimization, Anne Aaron, Netflix, Inc. (United States) [view abstract]

As Director of Encoding Technologies, Anne Aaron leads the team responsible for media processing and encoding at Netflix. Her team works on video, audio, images and timed-text -- from analysis to processing, encoding, packaging and DRM. On the streaming side, they strive to deliver a compelling viewing experience for millions of Netflix members worldwide, no matter where, how and what they watch. For the Netflix studio, they build media technologies that can improve content production. In her previous role at Netflix, Aaron led the Video Algorithms team. As a team, they researched and deployed innovation in the video encoding space (per-title encoding, video quality assessment and perceptual metrics, shot-based encoding, HDR, next-generation codecs) that benefited Netflix members as well as impacted the rest of the industry. Recent recognitions include: Some recent recognitions: SMPTE 2019 Workflow Systems Medal, Forbes' 2018 America's top women in Tech, Business Insider's 2017 Most powerful female engineers in US tech in 2017.

Audio and video compression are immensely important to Netflix, as well as internet service providers (ISPs). It has been estimated that our codec optimization efforts, together with the Open Connect program, saved ISPs over 1 billion dollars in 2021 alone. The keynote will talk about the importance of perceptual models and optimization for delivering the hits such as Stranger Things, Squid Game, or Red Notice in the highest quality while being mindful of the internet traffic. It will cover the recent advances in audio and video encoding, innovations in the subjective and objective assessment of quality, as well as immediate and future challenges in this area.

KEYNOTE: SD&A 2 (T2.1)

This session is jointly sponsored by: Engineering Reality of Virtual Reality 2023, and Stereoscopic Displays and Applications XXXIV.

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KEYNOTE: Human performance using stereo 3D in a helmet mounted display and association with individual stereo acuity, Bonnie Posselt, RAF Centre of Aviation Medicine (United Kingdom) [view abstract]

Wing Commander (Dr) Bonnie Posselt is a medical officer in the RAF (UK) specialising in Aviation and Space Medicine. Bonnie is currently based at the RAF Centre of Aviation Medicine in Bedfordshire, UK, having recently returned from a 3.5year exchange tour to Wright-Patterson Air Force Base in Ohio. While working with the 711th Human Performance Wing and the Air Force Research Laboratory (AFRL) in Ohio, Bonnie undertook a PhD in Helmet Mounted Displays and vision standards in collaboration with the University of Birmingham (UK). Bonnie is a graduate of the University of Manchester, King’s College London, and the International Space University. She is an associate fellow of the Aerospace Medical Association and elected member of the Royal Aeronautical Society.

Binocular Helmet Mounted Displays (HMDs) are a critical part of the aircraft system, allowing information to be presented to the aviator with stereoscopic 3D (S3D) depth, potentially enhancing situational awareness and improving performance. The utility of S3D in an HMD may be linked to an individual’s ability to perceive changes in binocular disparity (stereo acuity). Though minimum stereo acuity standards exist for most military aviators, current test methods may be unable to characterise this relationship. This presentation will investigate the effect of S3D on performance when used in a warning alert displayed in an HMD. Furthermore, any effect on performance, ocular symptoms, and cognitive workload shall be evaluated in regard to individual stereo acuity measured with a variety of paper-based and digital stereo tests.

KEYNOTE: Innovative Imaging Systems (T2)

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KEYNOTE: Metaphotonic routers for solid-state imaging: Making every photon count, Peter B. Catrysse, Stanford University (United States) [view abstract]

Dr. Peter B. Catrysse is a Senior Research Scientist in the E. L. Ginzton Laboratory (Stanford University). He holds a PhD and an MSc in Electrical Engineering from Stanford University. With his doctoral research, he pioneered the integration of subwavelength metal optics for color filtering in standard deep-submicron CMOS technology. His recent work focuses on metaphotonics at the interface between fundamental physics and imaging applications. Dr. Catrysse has published more than 120 peer-reviewed papers, presented over 40 invited talks, and has been awarded 8 patents. He was named one of “50 Tech” pioneers by the Belgian Financial Times (2017) and is featured in the top 1% leading Engineering and Technology Scientists on the academic portal Research (2022). Dr. Catrysse is a Fellow of the Optical Society (Optica), a Fellow of the SPIE, a Senior Member of the IEEE, and a Hoover Foundation Brussels Fellow of the BAEF.

Solid-state imaging relies on multiple optical functionalities, which are ideally photon efficient. Color is, for example, an important functionality in visible imaging. Achieving color functionality without loss of photons, however, represents a long standing challenge in integrated imaging systems. The standard approach uses absorbing color filters in a color filter array, which is very photon inefficient. We recently introduced the concept of a metaphotonic color router that overcomes this long standing challenge. A color router exploits the large number of degrees of freedom that are available when the optical stack region above the pixel photodetectors is nanopatterned with dielectric materials. It is a lossless device that routes all incident light based on color content directly, i.e., without any additional propagation, to the photodetectors. As a result, the color router can achieve color functionality without loss of photons, with a broadband, polarization-independent, and angularly robust response. In this talk, I will describe the color router as well as additional opportunities for metaphotonic routers.

KEYNOTE: Processing at the Edge (W1)

This session is jointly sponsored by: Computational Imaging XXI, and Imaging Sensors and Systems 2023.

8:45
COIMG/ISS Joint Sessions Welcome

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KEYNOTE: Deep optics: Learning cameras and optical computing systems, Gordon Wetzstein, Stanford University (United States) [view abstract]

Gordon Wetzstein is an Associate Professor of Electrical Engineering and, by courtesy, of Computer Science at Stanford University. He is the leader of the Stanford Computational Imaging Lab and a faculty co-director of the Stanford Center for Image Systems Engineering. At the intersection of computer graphics and vision, artificial intelligence, computational optics, and applied vision science, Prof. Wetzstein's research has a wide range of applications in next-generation imaging, wearable computing, and neural rendering systems. Prof. Wetzstein is a Fellow of Optica and the recipient of numerous awards, including an NSF CAREER Award, an Alfred P. Sloan Fellowship, an ACM SIGGRAPH Significant New Researcher Award, a Presidential Early Career Award for Scientists and Engineers (PECASE), an SPIE Early Career Achievement Award, an Electronic Imaging Scientist of the Year Award, an Alain Fournier Ph.D. Dissertation Award as well as many Best Paper and Demo Awards.

Neural networks excel at a wide variety of imaging and perception tasks, but their high performance also comes at a high computational cost and their success on edge devices is often limited. In this talk, we explore hybrid optical-electronic strategies to computational imaging that outsource parts of the algorithm into the optical domain or into emerging in-pixel processing capabilities. Using such a co-design of optics, electronics, and image processing, we can learn application-domain-specific cameras using modern artificial intelligence techniques or compute parts of a convolutional neural network in optics with little to no computational overhead. For the session: Processing at the Edge (joint with ISS).

KEYNOTE: AR/VR Special Session 1 (W1)

This session is jointly sponsored by: Engineering Reality of Virtual Reality 2023, Human Vision and Electronic Imaging 2023, and Stereoscopic Displays and Applications XXXIV.

Joint Conference Welcome

HVEI-219
KEYNOTE: Display consideration for AR/VR systems, Ajit Ninan, Reality Labs at Meta (United States) [view abstract]

Ajit Ninan is a display industry veteran and led the way to the industry adopting HDR. His inventions & innovations are manifest in millions of shipped HDR TV’s and consumer electronics from multiple companies. He holds 400+ granted patents in imaging and display technology and now works in imaging related to AR/VR at Meta as Senior Director of Applied Perceptual Science and Image Quality. His work spans multiple subjects ranging from Displays, Imaging, Color, Video, Compression, Audio and Networking. His career spans early start-ups to public companies. Ninan is the inventor of the local dimmed quantum dot TV and led the way to the industry adoption of quantum dot displays by working with Vizio, Nanosys and 3M to release the first of its kind R-series QD TV with HDR. He also led the effort with the JPEG committee to standardize JPEG-XT to enable JPEG HDR images. Ninan was inducted as a SMPTE Fellow for his contributions to imaging and standards. The display that caused the world to adopt HDR called the “Pulsar” capable of 4000nits down to .005nits with P3 color in 2010, built by Ninan and his team, has received many awards including the Advanced Imaging Society’s Lumiere award which enabled the development of Dolby Vision and earned Ninan an Emmy.

AR and VR displays must take into consideration human perception and image quality factors that are required for a product. At Meta, we study these perceptual factors and determine what quality targets and requirements are needed. This talk will discuss some of these aspects and highlight examples of our process that help us set direction. The presenter, Ajit Ninan, is the director of Engineering, Display and Optics, at Meta.

PANEL: High-Performance Computing in Imaging: from Academia to Industry (W1)

PANEL: AR/VR Special Session (W3.1)

This session is jointly sponsored by: Engineering Reality of Virtual Reality 2023, Human Vision and Electronic Imaging 2023, and Stereoscopic Displays and Applications XXXIV.

KEYNOTE: Systematic Data Labeling (W3.1)

3:30
Conference Welcome

3:35IPAS-284
KEYNOTE: Systematic data labeling at the point of ingestion in enterprise systems, Gevorg Karapetyan, Zero Cognitive Systems (United States) [view abstract]

Gevorg Karapetyan is co-founder and Chief Technology Officer with Zero Cognitive Systems. In this role Karapetyan leads long-term technology vision and is responsible for the direction, coordination, and delivery of technology. Founded in 2015 in Los Gatos, California, Zero is dedicated to applying artificial intelligence and smart automation to the most pressing operational challenges of the professional services industry. Karapetyan previously worked at Imagenomic as a Senior Software Engineer and attended National Polytechnic University of Armenia. Karapetyan holds a PhD in Computer Science and has more than 10 years of experience in developing intelligent automation systems.

Almost 80% of the enterprise data is unstructured. Unstructured data includes documents, emails, images, web pages, video files, audio files, etc., which are stored in different data silos. Classification of unstructured is an important topic for the world's largest enterprises. One of the approaches is labeling the content per particular project. We present a system for systematic labeling of the unstructured data at the point of ingestion. This approach gives the ability to systematically generate metadata from incoming unstructured data, which can be stored in data catalogs, unlocking the ability to get business insights from the data and reduce security risks.

KEYNOTE: Sensor Design II (W3)

3:30ISS-341
KEYNOTE: Event camera noise and denoising, Tobi Delbrück, Institute of Neuroinformatics, University of Zurich and ETH Zurich (Switzerland) [view abstract]

Tobi Delbrück (IEEE M'99, SM'06, F'13) received his BSc in physics from University of California in 1986 and his PhD from Caltech in 1993 as the first student with the Computation and Neural Systems program with PhD supervisor Carver Mead. He is an ETH Honorary Professor of Physics and Electrical Engineering, and has been with the Institute of Neuroinformatics, University of Zurich and ETH Zurich since 1998. The Sensors Group that he co-directs together with Prof. Shih-Chii Liu works on a broad range of topics covering device physics to computer vision and control, with a theme of efficient neuromorphic processing in hardware. He co-organizes the Telluride Neuromorphic Engineering workshop and has organized live demonstration sessions at ISCAS, NeuIPS, and AICAS and two conference Sessions at ISCAS. Delbrück is past Chair of the IEEE CAS Sensory Systems Technical Committee. He worked on electronic imaging at Arithmos, Synaptics, National Semiconductor, and Foveon and has co-founded 3 companies (Inilabs, Insightness and Inivation). His papers have been awarded 13 IEEE awards and he was named a Fellow of the IEEE Circuits and Systems Society for his work on neuromorphic sensors and processing. He likes to read storybooks, play tennis, and sometimes tries card magic.

Event cameras, like Dynamic Vision Sensors, mimic biology’s eyes. They output sparse, quick events rather than regular Nyquist samples, enabling vision systems that can respond quickly at low average power consumption, so that they can beat the usual power-latency tradeoff of frame-based vision. They have significant amounts of noise. How this noise arises and how to remove the noise without removing the signal is an interesting subject on which we have focused our research.

KEYNOTE: Visual Analytics (W3)

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KEYNOTE: Deep learning for scientific data analysis and visualization, Chaoli Wang, University of Notre Dame (United States) [view abstract]

Chaoli Wang is a Professor in Computer Science and Engineering at the University of Notre Dame. His primary research interests include scientific visualization (e.g., flow visualization, time-varying multivariate data visualization, deep learning for scientific visualization), visual analytics (e.g., learning analytics, visual analytics for scientific visualization, visual analytics applications), information visualization (e.g., graph visualization), and visualization in education. Wang received his PhD (2006) in Computer and Information Science from The Ohio State University.

Over the past five years, deep learning for scientific data analysis and visualization has quickly become a focused direction in visualization research. In this talk, I will discuss two of our works: TSR-TVD and CoordNet. TSR-TVD employs a recurrent generative network to produce temporal super-resolution of time-varying volumetric data. CoordNet leverages multilayer perceptrons to ingest coordinates and predicts quantities of interest, capable of completing different data generation and visualization generation tasks using the same network design and architecture. Finally, I will briefly introduce other works from my research group, provide an overview of state-of-the-art research, and outline opportunities and challenges for this vibrant research direction.

Human Vision and Electronic Imaging 2023

BANQUET: 2023 Friends of HVEI (W5)

HVEI-250
KEYNOTE: How to let your pictures shine! The impact of high dynamic range imaging on photography, Timo Kunkel, Dolby Laboratories, Inc. (United States) [view abstract]

Join us for a wonderful evening of conversations, a banquet dinner, and an enlightening speaker. This banquet is associated with the Human Vision and Electronic Imaging Conference (HVEI), but everyone interested in research at the intersection of human perception/cognition, imaging technologies, and art is welcome. Banquet registration required, online or at the registration desk. Location will be provided with registration.

Dr. Timo Kunkel is director of image technology & standards in the CTO office of Dolby Laboratories, Inc. His fields of expertise include image processing, color science, high dynamic range imaging, color appearance modeling, and advanced display technologies. Kunkel is engaged in developing color management models for both professional and consumer displays (dynamic range and gamut mapping concepts). This involves active research, code development and QA as well as applying metrological and psychophysical concepts for verification, icluding picture quality assessment and tuning for several display technologies from customers all over the world. Additionally, he has experience in neuroscience and psychological concepts related to the Human Visual System (signal processing in the retina and higher visual cortex), and has been involved in developing the core concepts of what is now Dolby Vision. Kunkel is also actively involved with international standards work, serving as technical expert and member of IEC TC100 (Audio, video and multimedia systems and equipment) and TC110 (Electronic displays), the International Color Consortium (ICC), as well as the SID International Committee of Display Metrology (ICDM). Further, Kunkel has a background in Physical Geosciences (remote sensing and geospatial image processing, GIS, Vegetation- and Ecosystem Modeling) and has worked in these fields with research departments at Lund University in Sweden, Lincoln University in New Zealand, and the University of Dar es Salaam in Tanzania. This work is supported by more than 20 years of experience as a freelance landscape and architecture photographer for clients in Europe and the US, winning several prizes with images combining HDR and computational photography aspects. Kunkel served as president of Bristol Chapter, ACM SIGGRAPHACM SIGGRAPH, 2006 - 2008, and was co-founder of the Bruder & Bär publishing company (Germany), serving there as Art Director, 2003 - 2006. Kunkel holds a PhD in computer science from University of Bristol, United Kingdom, and a MSc from University of Freiburg, Germany.

High-dynamic range imaging, better known by its acronym “HDR”, has established itself as a foundational component when looking at the aspects defining today’s image fidelity. Together with the availability of wide color gamut (WCG) approaches, HDR has influenced and shaped both the technical tools and the creative means of photography. This talk will touch on the intersection of HDR technologies and the artistic expression it enables, from scene lighting and composition via camera capture and processing, to print and display.

KEYNOTE: Vulnerability of Neural Networks (R2.1)

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KEYNOTE: Surprising vulnerability of neural networks: Recovering training and input data in federated learning and split computing, Pavlo Molchanov, NVIDIA Corporation (United States) [view abstract]

Pavlo Molchanov obtained his PhD (2014) from Tampere University of Technology, Finland, in the area of signal processing. His dissertation focused on designing automatic target recognition systems for radars. Since 2015 he has been with the Learning and Perception Research team at NVIDIA, currently holding a senior research scientist position. His research is focused on methods for neural network acceleration, and designing novel human-computer interaction systems and human understanding. On network acceleration, he is interested in neural network pruning methods and conditional inference. For human understanding he is working on landmark estimation, gesture recognition, hand pose estimation.

We present a number of studies that demonstrated the possibility of recovering training data distribution given only the final trained model. We also study the effect of data recovery in the split computing scenario where only intermediate features are shared. Finally, we will present results of gradient attack in federated learning that for a first time demonstrates almost the exact image recovery. The focus is on for large convolution networks such as ResNets and transformers, and on complex datasets such as ImageNet.