IMPORTANT DATES

2021
Journal-first submissions deadline
8 Aug
Priority submissions deadline 30 Jul
Final abstract submissions deadline 15 Oct
Manuscripts due for FastTrack publication
30 Nov

 
Early registration ends 31 Dec


2022
Short Courses
11-14 Jan
Symposium begins
17 Jan
All proceedings manuscripts due
31 Jan

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Image Processing: Algorithms and Systems XX

NOTES ABOUT THIS VIEW OF THE PROGRAM
  • Below is the the program in San Francisco time.
  • Talks are to be presented live during the times noted and will be recorded. The recordings may be viewed at your convenience, as often as you like, until 15 May 2022.

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, Dartmouth College (United States)

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, Jet Propulsion Laboratory (United States)

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
EI Symposium

Poster interactive session for all conferences authors and attendees.


IPAS-191
P-08: Class specific biased extrapolation of images in latent space for imbalanced image classification, Suhyeon Jeong and Seungkyu Lee, Kyung Hee University (Republic of Korea) [view abstract]

 

IPAS-192
P-09: Computer vision-based classification of schizophrenia patients from retinal imagery, Diana Joseph, Adriann Lai, Steven Silverstein, Rajeev Ramchandran, and Edgar Bernal, University of Rochester (United States) [view abstract]

 

IPAS-193
P-10: Optimal parameters selection of the Frost filter based on despeckling efficiency prediction for Sentinel SAR images, Oleksii S. Rubel1, Andrii S. Rubel1, Vladimir Lukin1, and Karen Egiazarian2; 1National Aerospace University (Ukraine) and 2Tampere University (Finland) [view abstract]

 

IPAS-194
P-11: Simulation-based virtual reality training for firefighters, Mohamed Saifeddine Hadj Sassi1, Federica Battisti2, and Marco Carli1; 1Roma Tre University and 2University of Padova (Italy) [view abstract]

 



Tuesday 25 January 2022

IS&T Awards & PLENARY: Physics-based Image Systems Simulation

07:00 – 08:15

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, Stanford Center for Image Systems Engineering, Stanford University, CEO and Co-founder, ImagEval Consulting (United States)

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

08:15 – 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.

Panel Moderator: Joyce Farrell, Stanford Center for Image Systems Engineering, Stanford University, CEO and Co-founder, ImagEval Consulting (United States)
Panelist: Matthias Neissner, Technical University of Munich (Germany)
Panelist: Paul Debevec, Netflix, Inc. (United States)
Panelist: Hany Farid, University of California, Berkeley (United States)


Image Filtering, Enhancement, and Object Detection

Session Chair: Karen Egiazarian, Tampere University (Finland)
09:15 – 10:20
Green Room

09:15
Conference Introduction

09:20IPAS-344
Contrast enhancement: Cross-modal learning approach for medical images, Rabia Naseem1, Akib J. Islam1,2, Faouzi Alaya Cheikh1, and Azeddine Beghdadi3; 1Norwegian University of Science and Technology (Norway), 2University Jean Monnet Saint-Etienne (France), and 3University Sorbonne Paris Nord (France) [view abstract]

 

09:40IPAS-345
Rapid circle detection through fusion of summative statistics of edge components, Scott A. Craver and Pheona Anjoy, Binghamton University (United States) [view abstract]

 

10:00IPAS-346
Training decision trees to guide feature selection for infrared image pre-screening algorithms, Dawne Deaver1 and Nader Namazi2; 1US Army DEVCOM C5ISR and 2The Catholic University of America (United States) [view abstract]

 



Multi-dimensional and Multimodal Image Processing Algorithms I

Session Chair: Karen Egiazarian, Tampere University (Finland)
10:45 – 11:45
Green Room

10:45IPAS-354
On properties of visual quality metrics in remote sensing applications, Oleg Ieremeiev1, Vladimir Lukin1, Krzysztof Okarma2, Karen Egiazarian3, and Benoit Vozel4; 1National Aerospace University (Ukraine), 2West Pomeranian University of Technology (Poland), 3Tampere University (Finland), and 4University of Rennes 1 (France) [view abstract]

 

11:05IPAS-355
Face detection and recognition in organic video: A comparative study for sport celebrities database, Yigit Oguzhan Akbay and Mihai Mitrea, Institut Mines-Telecom (France) [view abstract]

 

11:25IPAS-356
Volumetric reconstruction in Fourier-plane intregral microscopy, Sergio Moreschini, Robert Bregovic, and Atanas Gotchev, University of Tampere (Finland) [view abstract]

 



Multi-dimensional and Multimodal Image Processing Algorithms II

Session Chair: Sos Agaian, College of Staten Island and the Graduate Center, CUNY (United States)
15:00 – 16:00
Green Room

15:00IPAS-365
A frame level rate allocation algorithm based on temporal dependency model for AV1, Cheng Chen, Jingning Han, Paul Wilkins, and Yaowu Xu, Google Inc. (United States) [view abstract]

 

15:20IPAS-366
Alignment and fusion of visible and infrared images based on gradient-domain processing, Ayaka Tanihata, Masayuki Tanaka, and Masatoshi Okutomi, Tokyo Institute of Technology (Japan) [view abstract]

 

15:40IPAS-367
Deep reinforcement learning approach to predict head movement in 360° videos, Tanmay Ambadkar and Pramit Mazumdar, IIIT Vadodara (India) [view abstract]

 



Wednesday 26 January 2022

Signal and Image Classification I

Session Chair: Atanas Gotchev, Tampere University (Finland)
07:00 – 08:00
Green Room

07:00IPAS-381
Machine learning with blind imbalanced domains, Hiroshi Kuwajima1, Masayuki Tanaka2, and Masatoshi Okutomi2; 1DENSO Corporation and 2Tokyo Institute of Technology (Japan) [view abstract]

 

07:20IPAS-382
Real-time defect detection and classification on wood surfaces using deep learning, Mazhar Mohsin, Oluwafemi Samson Balogun, Keijo Haataja, and Pekka Toivanen, University of Eastern Finland (Finland) [view abstract]

 

07:40IPAS-383
Hair color digitization through imaging and deep inverse graphics, Robin Kips1,2, Panagiotis-Alexandros Bokaris1, Matthieu Perrot1, Pietro Gori2, and Isabelle Bloch3; 1L'Oréal Research and Innovation, 2LTCI, Telecom Paris, Institut Polytechnique de Paris, and 3Sorbonne Universite CNRS (France) [view abstract]

 



Signal and Image Classification II

Session Chair: Atanas Gotchev, Tampere University (Finland)
08:30 – 09:30
Green Room

08:30IPAS-390
Deep learning based udder classification for cattle traits analysis, Hina Afridi1,2, Mohib Ullah1, Øyvind Nordbø2, and Faouzi Alaya Cheikh1; 1Norwegian University of Science and Technology and 2GENO SA (Norway) [view abstract]

 

08:50IPAS-392
Expert training: Enhancing AI resilience to image coding artifacts, Alban Marie, Karol Desnos, Luce Morin, and Lu Zhang, Institut National des Sciences Appliquées de Rennes (France) [view abstract]

 



KEYNOTE: Perception and Image Quality

Session Chair: Atanas Gotchev, Tampere University (Finland)
10:00 – 11:00
Green Room

IPAS-399
KEYNOTE: Perception-guided image quality measurements: Principles and future trends, Sos S. Agaian, College of Staten Island and the Graduate Center, CUNY (United States)

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.


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