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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 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 military. 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 military 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, 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.

In the OTCBVS'10 workshop, we also want to emphasize more on Augmented Vision that requires processing data from many different types of signal and video sensors, including infrared, far infrared, millimeter wave, microwave, radar, and synthetic aperture radar sensors. Augmented Vision involves the creation of new and innovative approaches to the fields of signal processing and artificial intelligence. It is a fertile area for growth in both analysis and experimentation and includes both civilian and military applications. The availability of ever improving computer resources and continuing improvement in sensor performance has given great impetus to this field of research.

This series of OTCBVS workshop creates connections between different communities in the machine vision world ranging from public research institutes to private, military, and federal laboratories. It brings together academic pioneers, industrial and military 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:

In OTCBVS'10, special attention will be given to vision algorithms where non-visible sensors are employed. However, we also encourage the submission of high quality papers that deal with object tracking and classification in the visible spectrum. Additionally, emphasis will be placed on new and non-traditional applications of visible and non-visible imagery.

In second annual meeting of OTCBVS, a benchmark/test dataset of images and videos recorded in and beyond the visible spectrum is available here. The dataset is to be used to compare, evaluate, and adapt state‐of‐the‐art computer vision algorithms. It will fill out the lack of experimental non‐visible data in the vision community, and will allow a large spectrum of CVPR participants to explore the benefits of the non‐visible spectrum in real‐world applications, and contribute to OTCBVS workshop series. We also invite people to participate in the extension of this dataset.

Comparative evaluation studies across the non‐visible spectrum for a given computer vision or pattern recognition task are encouraged. Applications using non‐visible sensors from various domains are welcome. Sensors of interest include visible, infrared, millimeter wave, radar, and hyper‐spectral.

Topics of interested in OTCBVS'10 include:

Algorithms

• Target classification
• Feature extraction and matching
• Automatic object detection
• Object tracking
• Context understanding and recognition
• Face recognition in IR & thermal images

Sensors

• Smart systems with low SWAP
• Combining visible & non-visible signals
• Information fusion from disparate sensors
• Enhanced detection/tracking/recognition performance using fusion
• Vision augmentation enabling system concepts and components

Applications

• Medical applications
• Military application
• Automotive applications
• Driver vision enhancement systems
• Avionics systems with augmented vision

Important Dates :

• Paper Submission: March 14, 2010 (extended)
• Author Notification: April 05, 2010
• Camera Ready Submission: April 11, 2010
• OTCBVS Workshop: June 13, 2010

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