PBVS 2016 : 12th IEEE Workshop on Perception Beyond the Visible Spectrum Call for Papers

PBVS 2016 : 12th IEEE Workshop on Perception Beyond the Visible Spectrum
In Conjunction with CVPR 2016
Link: http://www.otcbvs.com
 
When	Jun 26, 2016 - Jun 26, 2016
Where	Las Vegas, NV, USA
Submission Deadline	Mar 7, 2016
Notification Due	Mar 21, 2016
Final Version Due	Mar 28, 2016
Categories    computer vision   machine learning   sensing   image processing
 
Call For Papers
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 OTCBVS) along its three main axes:
Algorithms, Sensors Processing, and Applications. This field 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. It also presents a
suitable framework for building solid advanced vision based systems.

The computer vision community has typically focused mostly on the
development of vision algorithms for object detection, tracking, and
classification associated with visible range sensors in day and
office-like environments. In the last decade, infrared (IR), thermal
and other non-visible imaging sensors were used only in special areas
like medicine and defense. That lower interest level in infrared
imagery was due in part to the high cost of non-visible range sensors,
low resolutions, poor image quality, lack of widely available data
sets, and lack of consideration of the potential advantages of the
non-visible part of the spectrum. These historical objections are
becoming less relevant as IR imaging technology is advancing rapidly
and the sensor cost is dropping dramatically. Image sensing devices
with high dynamic range and high 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 non-visible ranges are certainly
required. The fusion of visible and non-visible ranges, like radar and
IR images, or thermal and visible spectrum images as well as acoustic
images, is another dimension to explore for higher performance of
vision-based systems. The non-visible light is widely employed in
night vision-based systems, and many detection and recognition systems
available today in the market arerelying on physiological phenomena
produced by IR and thermal wavelengths. Using artificially controlled
lights is a practical solution to eliminate challenging ambient light
effects.

This series of Perception Beyond the Visible Spectrum workshops
creates connections between different communities in the machine
vision world ranging from public research institutes to private,
defense, and federal laboratories. It brings together academic
pioneers, industrial and defense researchers and engineers in the
field of computer vision, image analysis, pattern recognition, machine
learning, signal processing, sensors, and human-computer interaction.



Topics of Interest: 

Sensing/Imaging Technologies--- 
IR/EO imaging system 
Underwater sensing 
Hyperspectral/Satellite imaging 
Spectroscopy/Microscopy imaging 
LIDAR/LDV sensing 
Compressive sensing 
RADAR/SAR imaging 
RGBD sensing 

Applications and Systems--- 
Surveillance and reconnaissance systems 
Autonomous vehicles 
Autonomous ships 
Autonomous grasping 
Vision-aided navigation 
Night/Shadow vision 
Sensing for agriculture and food safety 
Vision-based autonomous multi-copter 

Theory and Algorithms--- 
Imagery/Video exploitation 
Object/Target tracking and recognition 
Feature extraction and matching 
Activity recognition 
Deep learning for perception 
Multimodal/Multi-sensor data fusion 
Multimodal registration 
Video + text fusion