The Conference on Computer Vision and Pattern Recognition is where you will find the largest gathering of computer vision, machine learning, and artificial intelligence professionals. As the premier annual computer
The Conference on Computer Vision and Pattern Recognition is where you will find the largest gathering of computer vision, machine learning, and artificial intelligence professionals. As the premier annual computer vision conference in the world, Kitware’s computer vision team has had a long-term commitment to CVPR.
We are proud to have a paper accepted at CVPR 2021. In addition, Kitware is sponsoring the main conference, helping co-organize and present at the full-day workshop on media forensics, and is sponsoring the EarthVision workshop. The event details are listed below. For more information regarding our involvement at CVPR 2021, our computer vision capabilities, and our deep-rooted connection to this community, visit our blog.
Workshop on Media Forensics
Date: Saturday, June 19, 2021 (full-day)
Media forensics has recently become a topic of interest within the computer vision community as a result of the increasing prevalence of fabricated media spreading misinformation. And now, with the emergence of more sophisticated machine learning and computer vision techniques, media forensics has become a broad and prominent area of research. With the emergence of more sophisticated machine learning and computer vision techniques, these experts will discuss the importance of advancing media forensics. Click for more information about the workshop.
Scott McCloskey, assistant director of computer vision at Kitware, is a co-organizer and co-presenter for this workshop. Arslan Basharat, assistant director of computer vision at Kitware, is on the technical committee for this workshop.
EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
Date: Sunday, June 19, 2021 (full day)
Earth observation and remote sensing depend on computer vision, machine learning, and signal/image processing. Together, they provide large-scale, homogeneous information about processes occurring at the Earth’s surface, which can largely impact human society, economy, industry, and the planet. This workshop aims to raise awareness of this highly challenging and quickly evolving field of research and to foster collaboration between the computer vision and Earth observation communities. Click for more information about the workshop.
Matt Leotta, a technical lead for Kitware’s Computer Vision Team, is on the technical committee for this workshop.
Paper Presentation: Discovering Hidden Physics Behind Transport Dynamics
Date: Wednesday, June 23, 2021 from 10 pm – 12:30 am
Transport processes are ubiquitous. They are, for example, at the heart of optical flow approaches; or of perfusion imaging, where blood transport is assessed, most commonly by injecting a tracer. An advection-diffusion equation is widely used to describe these transport phenomena. Our goal is to estimate the underlying physics of advection-diffusion equations, expressed as velocity and diffusion tensor fields. We propose a learning framework (YETI) building on an auto-encoder structure between 2D and 3D image time-series, which incorporates the advection-diffusion model. To help with identifiability, we develop an advection-diffusion simulator which allows pre-training of our model by supervised learning using the velocity and diffusion tensor fields. Instead of directly learning these velocity and diffusion tensor fields, we introduce representations that assure incompressible flow and symmetric positive semi-definite diffusion fields and demonstrate the additional benefits of these representations on improving estimation accuracy. We further use transfer learning to apply YETI on a public brain magnetic resonance (MR) perfusion dataset of stroke patients and show its ability to successfully distinguish stroke lesions from normal brain regions via the estimated velocity and diffusion tensor fields. Click for more information.
Stephen Aylward, senior director of strategic initiatives at Kitware, is a co-author of this paper.
To learn more about Kitware’s computer vision work, please reach out to firstname.lastname@example.org. We look forward to engaging with this community and sharing information about Kitware’s ongoing research and capability development in computer vision and deep learning, as well as our cutting-edge open source software.
june 19 (Saturday) - 25 (Friday)