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Date31oct - 2novoct 318:00 amnov 2NSSDF 2017Joint Meeting of the Military Sensing Symposium (MSS) Specialty Groups on Battlespace Acoustic, Seismic, Magnetic, and Electric-Field Sensing and Signatures (BAMS) and the National Symposium on Sensor Data & Fusion
This year’s 2017 Joint Meeting of the Military Sensing Symposium (MSS) Specialty Groups on Battlespace Acoustic, Seismic, Magnetic, and Electric-Field Sensing and Signatures (BAMS) and [...]
This year’s 2017 Joint Meeting of the Military Sensing Symposium (MSS) Specialty Groups on Battlespace Acoustic, Seismic, Magnetic, and Electric-Field Sensing and Signatures (BAMS) and the National Symposium on Sensor Data & Fusion (NSSDF) will be held October 31 through November 2 in Springfield, VA. This notable Department of Defense (DoD) conference, sponsored by Army RDECOM ARMDEC, Army RDECOM CERDEC, Army Research Laboratory (ARL), Air Force Research Laboratory (AFRL), Office of Naval Research (ONR), and the Office of the Undersecretary of Defense will be held at the National Geospatial Intelligence Agency (NGA) and will include presentations, poster sessions, and technology specific sessions discussing critical technologies being developed and implemented for the DoD.
Kitware, a Bronze sponsor for this event, will be in attendance and has been selected to provide four technical presentations in the NSSDF meetings on October 31 and November 1. NSSDF will focus on areas such as cyber situational awareness and situational understanding, methods for improved target tracking, new methods in data fusion, and disruptive technologies. Kitware’s presentation titled “Fusing Visible and Infrared (IR) Video on Mobile Robots, UAVs and Warfighters for Real-Time, Squad-Level Situational Awareness” will be briefed on October 31 under Session N1D: Fusion. A Kitware Computer Vision team member will discuss Kitware’s ongoing technology development to improve Intelligence, Surveillance, and Reconnaissance (ISR) capabilities of Army and Marine rifle squads, sponsored by the DARPA Squad-X Core Technologies program. Kitware and its collaborators, the University of Maryland and the University of Pennsylvania, are developing the THreat Reconnaissance and Exploitation from Audio-Video Target eXtraction (THREAT X) system, which focuses on utilizing unmanned systems, and fusing various sensors (Infrared (IR) and RGB) to build automated, intelligence video processing and alert mechanisms supporting the soldier on the ground without added burden. Kitware has performed multiple data collections over extended periods of time in order to successfully build deep learning computer vision techniques to address person detection and threat classification in battlefield scenarios. A quantified assessment of this system will be included. Authors contributing to this project and this paper include Anthony Hoogs, Ph.D., Keith Fieldhouse, and Eran Swears, Ph.D.; all part of Kitware’s Computer Vision Team.
November 1, under Session N2B: Disruptive, Game-Changing Systemic and Technological Concepts, Kitware will brief Deep Learning for Object Detection and Object-based Change Detection in Satellite Imagery. This presentation will discuss some of Kitware’s technological contributions to the Visual Global Intelligence and Analytics Toolkit (VIGILANT) program, funded by the Air Force. Kitware is building an open, extensible software framework and demonstration system that will provide cutting-edge automated change detection, tipping, and cueing mechanisms for exploitation of commercial satellite imagery and video. The system will integrate existing state-of-the-art algorithms from Kitware and the Rochester Institute of Technology (RIT) for satellite image registration, object detection, and object-based change detection incorporating the latest deep learning techniques to enable rapid adaptation to new changes, objects and/or sensor characteristics using the DIRSIG simulation tool. VIGILANT is built on Kitware-developed open source frameworks for distributed processing and management of heterogeneous big data sources and leverages Kitware’s geographic visualization and analytics capabilities packaged into a modern, browser-based dynamic user interface. Authors contributing to this project and this paper include Charles Law, Ph.D., Jason Parham, Ph.D., Matt Dawkins, Paul Tunison, David Stoup, Rusty Blue, Ph.D., Keith Feildhouse, Matt Turek, Ph.D., Anthony Hoogs, Ph.D., all from Kitware; S. Han, A. Farafard, J. Kerekes, E. Lentilucci, M. Gartley, A Savakis, T. Rovito, all from RIT; and S. Thomas and C. Stansifer from the Air Force Research Lab (AFRL).
Under Session N2C: Emerging Threats and Challenge Domains, Kitware will present on Automatic Pattern of Life Learning in Satellite Images through Graph Kernels. This presentation will provide specific details and methods that are successfully being used to learn typical observed patterns in satellite images such as types and density of vehicles at a specific time of day. Learning patterns can be very challenging due to issues related to different imaging conditions such as resolution, view angle, and sensor type modalities, like Electro-Optical (EO), Infrared (IR), and Hyperspectral Imagery (HSI). To address these, Kitware will brief a new, robust, and scalable approach based on graph modeling and rapid comparison through graph kernels to identify typical patterns in large satellite images. This approach builds upon the contents extracted automatically from the satellite images through deep learning models for object detection and semantic scene segmentation. Authors include John Moeller, Ph.D., Eric Smith, Ph.D., Arslan Basharat, Ph.D., Matt Turek, Ph.D., Anthony Hoogs, Ph.D., all from Kitware, and Erik Blasch from the Air Force Office of Scientific Research (AFOSR).
SegNet deep learning model for semantic segmentation has been extended for satellite images.
Finally, Under Session N2C, Kitware will brief on Using Convolutional Neural Networks (CNNs) for Content-Based Full Motion Video (FMV) Retrieval. The amount of FMV data available to analysts due to the rapid growth in development and use of Unmanned Aerial Systems (UAS) has dramatically increased. This poses many problems as automated analytic capabilities have not kept pace and there are very few tools available for alerting or archive search due to challenges related to low resolution, appearance variability, complex scenes, shadows, and other factors. Deep learning shows great promise in automating the fundamental tasks of FMV, including object recognition, object-based retrieval, and image search. Kitware will discuss Hierarchical Dynamic Video Exploitation (HiDyVE), which uses CNNs, a form of deep learning for imagery, to detect and describe objects in FMV streams and archives. The CNNs are used for automatic target recognition when training data per object type is available, and for video search. This system will be detailed and demonstrated on FMV data in order to show the audience how deep learning improves these capabilities vs. a traditional, non-deep learning approach. Authors include Matt Dawkins, Roddy Collins, Ph.D., and Anthony Hoogs, Ph.D., all from Kitware’s Computer Vision team.
Kitware’s Computer Vision group is a leader in building state-of-the-art algorithms and software for automated image and video analytics. They work extensively with deep learning and computer vision to develop innovative solutions supporting the DoD. Key focus areas include object detection and tracking, complex activity, event, and threat detection, image and video scene understanding, and image and video forensics. Kitware’s open source business model and the Computer Vision group’s development of the KitWare Image and Video Exploitation and Retrieval Toolkit permits the DoD Intelligence and Operations communities the opportunity to utilize cutting edge technology bridging the gap between research code, production software, and operational evaluation. It helps avoid expensive software duplication and redundancy, pushing our partners to accelerate development of newer, better capabilities to deliver to the warfighter sooner.
Please reach out to email@example.com to set up a discussion with Matt Turek, Keith Fieldhouse, and Rusty Blue during this meeting in order to discuss Kitware’s capabilities and technology contributions to the DoD.
October 31 (Tuesday) 8:00 am - November 2 (Thursday) 5:00 pm1nov6:00 pm- 8:30 pmPyData Triangle Q4-2017 Meetup
Kitware's Matt McCormick will be speaking at the PyData Triangle Meetup in Morrisville, NC on Nov 1, 2017. His presentation is titled "Multi-Dimensional, Multi-Modal [...]
(Wednesday) 6:00 pm - 8:30 pm
3020 Carrington Mill Boulevard, Suite 300, Morrisville, NC
Eric Dill, Chris Calloway and Ginny Ghezzo Organizers may be contexted through Meetup.com messages.2nov1:00 am- 1:00 amKitware @ VISE Fall 2017 Seminar SeriesVanderbilt Institute for Surgery and Engineering
On November 2, 2017, Dr. Stephen Aylward will present a seminar as part of VISE's Fall [...]
Title: Open Science and Computer-Augmented Point-of-Care Ultrasound.
Summary: Open science is a bridge between academia and industry, and it is a way to succeed in academia and industry. The first part of this talk will discuss the challenges and opportunities of open science and how open science can be incorporated into your work, to increase its impact and lead to success. The second part of this talk discusses point-of-care ultrasound. Dr. Aylward’s work in this area is driven by the premise that for the full potential of point-of-care ultrasound to be realized, these systems must be approached as if they were new diagnostic modalities, not simply as inexpensive portable ultrasound imaging systems. These systems must incorporate automated data analysis algorithms, rugged hardware, and specialized interfaces to guide novice users to properly place and manipulate an ultrasound probe and interpret its outputs. Furthermore, the outputs of a point-of-care ultrasound system should be quantitative measures and easy-to-understand reformulations of the acquired data, not b-mode images. It should be assumed that the expertise needed to interpret b-mode images will not be readily available at a point of care, e.g., when used by emergency medical service personnel or military medics to triage trauma patients or when used in schools to screen for scoliosis.
The seminar begins at 12:10 pm at Stevenson Center 5326. For more information, see the VISE Fall 2017 Seminar Series website or contact firstname.lastname@example.org.
(Thursday) 1:00 am - 1:00 am
Stevenson Center 5326
2301 Vanderbilt Place Nashville, Tennessee 37235-182412nov - 17All DaySupercomputing 2017The International Conference for High Performance Computing, Networking, Storage and Analysis13nov8:45 am- 5:00 pm2017 USGIF Machine Learning and Artificial Intelligence Workshop
The Machine Learning & Artificial Intelligence (AI) Workshop, sponsored by the United States Geospatial Intelligence Foundation (USGIF), will be held on November 13
The Machine Learning & Artificial Intelligence (AI) Workshop, sponsored by the United States Geospatial Intelligence Foundation (USGIF), will be held on November 13th in Springfield, VA. Organized by the USGIF Machine Learning and AI Working Group, the workshop will highlight technical leaders discussing challenges and strategic initiatives focused on AI, machine learning, cognitive computing, and deep learning in geospatial intelligence.
Kitware’s Senior Director of Computer Vision, Dr. Anthony Hoogs, has been invited to participate in the panel titled “Hard Problems of Interest” scheduled for 12:45-13:30. This classified break out session will also include professionals from Riverside Research, National Reconnaissance Office (NRO), Office of the Assistance Secretary of Defense for Research and Engineering (ASD) Department of Defense (DoD), and John Hopkins University Applied Physics Laboratory. Dr. Hoogs will discuss hard deep learning problems in imagery such as generalizing from few training examples, higher-level reasoning and multi-INT fusion. Kitware’s Computer Vision Group has significant experience in solving hard problems using deep learning with limited training examples, on satellite, aerial and ground-based imagery for geospatial intelligence.
Please reach out to email@example.com to schedule a time to meet with Anthony at this timely, unique workshop. We look forward to seeing you there.
(Monday) 8:45 am - 5:00 pm
NGA Campus East, Springfield, VA
7500 GEOINT Dr, Springfield, VA 22150
USGIF firstname.lastname@example.org - 30nov 287:00 amnov 302017 Chemical and Biological Defense Science & Technology ConferenceCBD S&T Conference
During Tuesday’s poster session, Technical Leader Aashish Chaudhary will present “Geospatial Utilities and Models for Biosurveillance Operations” in Exhibit Hall B.
28 (Tuesday) 7:00 am - 30 (Thursday) 4:00 pm PST
Long Beach Convention and Entertainment Center
300 E Ocean Blvd, Long Beach, CA 9080230nov - 1decAll DayMolSSI Workshop: Quantum Chemistry SchemaThe Molecular Sciences Software Institute
By invitation from the Molecular Sciences Software Institute, Dr. Marcus Hanwell is co-organizing and leading a workshop at Lawrence Berkeley National Laboratory on Nov 30 - Dec 1, 2017. Dr. [...]
By invitation from the Molecular Sciences Software Institute, Dr. Marcus Hanwell is co-organizing and leading a workshop at Lawrence Berkeley National Laboratory on Nov 30 – Dec 1, 2017. Dr. Hanwell’s workshop will be titled “Quantum Chemistry Schema,” and his co-organizers are Dr. Daniel G. A. Smith of MolSSI, Dr. Bert de Jong of LBNL, and Dr. Aaron Virshup of Autodesk.
From the event page: “The goal of this workshop is to produce a schema that will provide API-like access to pre-existing quantum chemistry packages to enable more complex workflows. … This workshop will pull together a large group of academic and commercial program developers to refine the overall scope and desired aspect of the schema. For further information and current discussion please view the schema’s GitHub page. “
November 30 (Thursday) - December 1 (Friday)
Lawrence Berkeley National Laboratory
1 Cyclotron Road Berkeley, CA, 94720, United States
Dr. Daniel G. A. Smith (MolSSI), Dr. Bert de Jong (LBNL), Dr. Marcus Hanwell (Kitware), and Dr. Aaron Virshup (Autodesk)
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