Monday 17 January 2022
IS&T Welcome & PLENARY: Quanta Image Sensors: Counting Photons Is the New Game in Town
07:00 – 08:10
The Quanta Image Sensor (QIS) was conceived as a different image sensor—one that counts photoelectrons one at a time using millions or billions of specialized pixels read out at high frame rate with computation imaging used to create gray scale images. QIS devices have been implemented in a CMOS image sensor (CIS) baseline room-temperature technology without using avalanche multiplication, and also with SPAD arrays. This plenary details the QIS concept, how it has been implemented in CIS and in SPADs, and what the major differences are. Applications that can be disrupted or enabled by this technology are also discussed, including smartphone, where CIS-QIS technology could even be employed in just a few years.
Eric R. Fossum is best known for the invention of the CMOS image sensor “camera-on-a-chip” used in billions of cameras. He is a solid-state image sensor device physicist and engineer, and his career has included academic and government research, and entrepreneurial leadership. At Dartmouth he is a professor of engineering and vice provost for entrepreneurship and technology transfer. Fossum received the 2017 Queen Elizabeth Prize from HRH Prince Charles, considered by many as the Nobel Prize of Engineering “for the creation of digital imaging sensors,” along with three others. He was inducted into the National Inventors Hall of Fame, and elected to the National Academy of Engineering among other honors including a recent Emmy Award. He has published more than 300 technical papers and holds more than 175 US patents. He co-founded several startups and co-founded the International Image Sensor Society (IISS), serving as its first president. He is a Fellow of IEEE and OSA.
08:10 – 08:40 EI 2022 Welcome Reception
Wednesday 19 January 2022
IS&T Awards & PLENARY: In situ Mobility for Planetary Exploration: Progress and Challenges
07:00 – 08:15
This year saw exciting milestones in planetary exploration with the successful landing of the Perseverance Mars rover, followed by its operation and the successful technology demonstration of the Ingenuity helicopter, the first heavier-than-air aircraft ever to fly on another planetary body. This plenary highlights new technologies used in this mission, including precision landing for Perseverance, a vision coprocessor, new algorithms for faster rover traverse, and the ingredients of the helicopter. It concludes with a survey of challenges for future planetary mobility systems, particularly for Mars, Earth’s moon, and Saturn’s moon, Titan.
Larry Matthies received his PhD in computer science from Carnegie Mellon University (1989), before joining JPL, where he has supervised the Computer Vision Group for 21 years, the past two coordinating internal technology investments in the Mars office. His research interests include 3-D perception, state estimation, terrain classification, and dynamic scene analysis for autonomous navigation of unmanned vehicles on Earth and in space. He has been a principal investigator in many programs involving robot vision and has initiated new technology developments that impacted every US Mars surface mission since 1997, including visual navigation algorithms for rovers, map matching algorithms for precision landers, and autonomous navigation hardware and software architectures for rotorcraft. He is a Fellow of the IEEE and was a joint winner in 2008 of the IEEE’s Robotics and Automation Award for his contributions to robotic space exploration.
Image Processing: Algorithms and Systems XX Posters
08:20 – 09:20
Poster interactive session for all conferences authors and attendees.
P-08: Class specific biased extrapolation of images in latent space for imbalanced image classification, [view abstract]
P-09: Computer vision-based classification of schizophrenia patients from retinal imagery, [view abstract]
P-10: Optimal parameters selection of the Frost filter based on despeckling efficiency prediction for Sentinel SAR images, [view abstract]
P-11: Simulation-based virtual reality training for firefighters, [view abstract]
Tuesday 25 January 2022
IS&T Awards & PLENARY: Physics-based Image Systems Simulation
07:00 – 08:00
Three quarters of a century ago, visionaries in academia and industry saw the need for a new field called photographic engineering and formed what would become the Society for Imaging Science and Technology (IS&T). Thirty-five years ago, IS&T recognized the massive transition from analog to digital imaging and created the Symposium on Electronic Imaging (EI). IS&T and EI continue to evolve by cross-pollinating electronic imaging in the fields of computer graphics, computer vision, machine learning, and visual perception, among others. This talk describes open-source software and applications that build on this vision. The software combines quantitative computer graphics with models of optics and image sensors to generate physically accurate synthetic image data for devices that are being prototyped. These simulations can be a powerful tool in the design and evaluation of novel imaging systems, as well as for the production of synthetic data for machine learning applications.
Joyce Farrell is a senior research associate and lecturer in the Stanford School of Engineering and the executive director of the Stanford Center for Image Systems Engineering (SCIEN). Joyce received her BS from the University of California at San Diego and her PhD from Stanford University. She was a postdoctoral fellow at NASA Ames Research Center, New York University, and Xerox PARC, before joining the research staff at Hewlett Packard in 1985. In 2000 Joyce joined Shutterfly, a startup company specializing in online digital photofinishing, and in 2001 she formed ImagEval Consulting, LLC, a company specializing in the development of software and design tools for image systems simulation. In 2003, Joyce returned to Stanford University to develop the SCIEN Industry Affiliates Program.
PANEL: The Brave New World of Virtual Reality
08:00 – 09:00
Advances in electronic imaging, computer graphics, and machine learning have made it possible to create photorealistic images and videos. In the future, one can imagine that it will be possible to create a virtual reality that is indistinguishable from real-world experiences. This panel discusses the benefits of this brave new world of virtual reality and how we can mitigate the risks that it poses. The goal of the panel discussion is to showcase state-of-the art synthetic imagery, learn how this progress benefits society, and discuss how we can mitigate the risks that the technology also poses. After brief demos of the state-of-their-art, the panelists will discuss: creating photorealistic avatars, Project Shoah, and digital forensics.
Image Filtering, Enhancement, and Object Detection
Karen Egiazarian, Tampere University (Finland)
09:15 – 10:20
Contrast enhancement: Cross-modal learning approach for medical images, [view abstract]
Rapid circle detection through fusion of summative statistics of edge components, [view abstract]
Training decision trees to guide feature selection for infrared image pre-screening algorithms, [view abstract]
Multi-dimensional and Multimodal Image Processing Algorithms I
Karen Egiazarian, Tampere University (Finland)
10:45 – 11:45
On properties of visual quality metrics in remote sensing applications, [view abstract]
Face detection and recognition in organic video: A comparative study for sport celebrities database, [view abstract]
Volumetric segmentation for integral microscopy with Fourier plane recording, [view abstract]
Multi-dimensional and Multimodal Image Processing Algorithms II
Sos Agaian, College of Staten Island and the Graduate Center, CUNY (United States)
15:00 – 16:00
A frame level rate allocation algorithm based on temporal dependency model for AV1, [view abstract]
Alignment and fusion of visible and infrared images based on gradient-domain processing, [view abstract]
Deep reinforcement learning approach to predict head movement in 360° videos, [view abstract]
Wednesday 26 January 2022
Signal and Image Classification I
Atanas Gotchev, Tampere University (Finland)
07:00 – 08:00
Machine learning with blind imbalanced domains, [view abstract]
Real-time defect detection and classification on wood surfaces using deep learning, [view abstract]
Hair color digitization through imaging and deep inverse graphics, [view abstract]
Signal and Image Classification II
Atanas Gotchev, Tampere University (Finland)
08:30 – 09:30
Deep learning based udder classification for cattle traits analysis, [view abstract]
Expert training: Enhancing AI resilience to image coding artifacts, [view abstract]
Accuracy evaluation of methods for pose estimation from fiducial markers, [view abstract]
KEYNOTE: Perception and Image Quality
Session Chair: Atanas Gotchev, Tampere University (Finland)
10:00 – 11:00
KEYNOTE: Perception-guided image quality measurements: Principles and future trends [PRESENTATION-ONLY],
Bio-inspired image processing is about learning image algorithms from computational neuroscience, cognitive science, and biology and applying them to the design of real-world image processing-based systems. More specifically, this field is giving computers the ability to "see" just as humans do. Recently, many useful image processing algorithms developed with varying degrees of correspondence with biological vision studies. This is natural since a biological system can provide a source of inspiration for new computational efficient/robust vision models and measurements. Simultaneously, the image processing tools may give new insights for understanding biological visual systems. Digital images are subject to various distortions during acquisition, processing, transmission, compression, storage, and reproduction. How can we automatically predict quantitatively or perceived image quality? In this talk, we present originating in visual perception studies: Visual perception-driven image quality measurements: principles, future trends, applications. We will also give our recent research works and a synopsis of the current state-of-the-art results in image quality measurements and discuss future trends in these technologies and the associated commercial impact and opportunities.
Sos S. Agaian is a distinguished professor of computer science at CSI and the Graduate Center, CUNY. Dr. Agaian was a Peter T. Flawn Professor of the University of Texas at San Antonio. His research sponsors include DARPA, NSF, US Department of Transportation, US Department of Energy, NIJ, and private industry. Dr. Agaian’s research interests are in big and small data analytics, computational vision and sensing, machine learning and urban computing, multimodal biometric and digital forensics, information processing and fusion, and fast algorithms. He has special interests in finding meaning in visual content-examine images for faces, text, objects, action, sciences, and other contents; and in the development of scientific systems and architectures in the theory and practice of engineering and computer sciences (emphasizing complex digital data processing, information sciences and systems technologies in the military, as well as medical and industrial information processing centers). Dr. Agaian has developed applications in healthcare, biomedical data mining, object recognition, signal processing, computer-aided food quality inspection, 3D imaging visible and thermal sensors, computational photography, multimedia security, needs-driven medical and biomedical technology, finance, and other related areas. He has published 750 articles, 10 books, 19 book chapters, and holds more than 56 American and foreign issued or pending patents/ disclosures. Several of Agaian’s IP are commercially licensed. He is an Associate Editor for several journals, including the Image processing Transaction (IEEE) and IEEE Transaction of Cybernetics. He is a fellow of IS&T, SPIE, AAAS, IEEE, and AAI. Dr. Agaian gave more than 15 plenary/keynote speeches and 50+ Invited talks.