IEEE Workshop on Perception Beyond the Visible Spectrum (PBVS 2020) Call for Papers

16th IEEE Workshop on Perception Beyond the Visible Spectrum (PBVS 2020), 
in Conjunction with CVPR 2020
June 2020
Seattle, WA, USA
Important Dates

    Paper Submission: March 13, 2020
    Author Notification: April 5, 2020
    Camera Ready: April 12, 2020
    PBVS Workshop: June 14, 2020



Objective & Scope

Download the CFP

The objective of this workshop is to highlight cutting edge advances
and state-of-the-art work being made in the exponentially growing
field of PBVS (previously “Object Tracking & Classification Beyond
the Visible Spectrum” - OTCBVS) integrating sensor processing,
algorithms, and applications. PBVS involves deep theoretical research
in sub-areas of image processing, machine vision, pattern recognition,
machine learning, robotics, and augmented reality within and beyond
the visible spectrum. Advancing vision-based systems includes
frameworks and methods featured in PBVS.

The computer vision community has typically focused mostly on the
development of vision algorithms for object detection, tracking, and
classification with visible range sensors in day and office-like
environments. In the last decade, infrared (IR), depth, thermal and
other non-visible imaging sensors were used only in special area like
medicine and defense. The relatively lower interest level in those
sensory areas in comparison to computer vision was due in part to
their high cost, low resolutions, poor image quality, lack of widely
available data sets, and/or lack of consideration of the potential
advantages of the non-visible part of the spectrum. These objections
are becoming overcome as sensory technology is advancing rapidly and
the sensor cost is dropping dramatically. Image sensing devices with
high dynamic range and IR sensitivity have started to appear in a
growing number of applications ranging from defense and automotive
domains to home and office security.

We encourage the submission of original papers that cover the topics
of interest mentioned below. In order to develop robust and accurate
vision-based systems that operate in and beyond the visible spectrum,
not only existing methods and algorithms originally developed for the
visible range should be improved and adapted, but also entirely new
algorithms that consider the potential advantages of nonvisible ranges
are certainly required. The fusion of visible and non-visible ranges,
like radar and IR images, depth images or IMU information, or thermal
and visible spectrum images as well as acoustic images, is another
dimension to explore for higher performance of vision-based systems.

This 16th IEEE CVPR Workshop on Perception Beyond the Visible Spectrum
(PBVS’2020) fosters connections between communities in the machine
vision world ranging from public research institutes to private,
defense, and federal laboratories. PBVS brings together academic
pioneers, industrial and defense researchers and engineers in the
field of computer vision, image analysis, pattern recognition, machine
learning, signal processing, artificial intelligence, sensor
exploitation, and HCI.

PBVS'2020 is accompanied by the first Thermal Image Super-Resolution
(TISR'2020) challenge. For more information about the challenge,
the dataset, the evaluation approach and measures as well as the
deadline for participation, please visit the workshop website.
Topics of Interest

Sensing/Imaging Technologies

    IR/EO/RGBD imaging system
    Underwater sensing
    Multi-spectral/Satellite imaging
    Spectroscopy/Microscopy imaging
    LIDAR/LDV sensing
    Compressive sensing
    RADAR/SAR imaging
    Radiation sensing
    Active imaging; Cooperative Sensing

Applications and Systems

    Surveillance and reconnaissance systems
    Unmanned autonomous systems
    Vehicle, ship, object classification
    Robotic grasping
    Vision-aided navigation and SLAM
    Night/Shadow vision
    Sensing for agriculture and food safety
    Vision-based autonomous aerial vehicles
    Lifelong & Robust machine learning

Theory and Algorithm

    Deep Learning, Reinforcement Learning
    Imagery/Video exploitation
    Object/Target tracking and recognition
    Feature extraction and matching
    Activity/Pattern learning and recognition
    Multimodal/Multi-sensor/INT fusion
    Multimodal Geo-registration
    3D Reconstruction and shape modeling
    Automatic caption generation; Data labeling