14th IEEE Workshop on Perception Beyond the Visible Spectrum Call for Papers

Full Title:   14th IEEE Workshop on Perception Beyond the Visible Spectrum
Short Title:  PBVS 2018
When:  Jun 18, 2018
Where:   Salt Lake City, Utah, USA
Submission Deadline:   Mar 4, 2018 
Notification Due: Mar 18, 2018 
Final Version Due:   Apr 8, 2018
Website:  www.otcbvs.com

Objective & Scope

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 historical
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. In addition, mobile
hyperspectral and mm-wave sensors are also coming into existence.

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. For example,
non-visible light is widely employed in night vision-based systems,
and many detection and recognition systems available today rely on
physiological phenomena produced by IR and thermal wavelengths. Using
artificially controlled lights is a practical solution to eliminate
challenging ambient light effects. In active imaging for example, near
or short-wave IR laser illumination can even be utilized to see
through dust/fog.

This 14th IEEE CVPR WS on Perception Beyond the Visible Spectrum
(PBVS’2018) 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.

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