Reliable tracking of multiple moving objects in video is an interesting challenge, made difficult in real-world video by various sources of noise and uncertainty. We propose a Bayesian approach to find correspondences between moving objects over frames. By using color values and position information of the moving objects as observations, we probabilistically assign tracks to those objects. We allow for tracks to be lost and then recovered when they resurface. The probabilistic assignment method, along with the ability to recover lost tracks, adds robustness to the tracking system. We present results that show that the Bayesian method performs well in difficult tracking cases and compare the probabilistic results to a Euclidean distance based method.

The authors define the concept of information fusion and show how they used it to estimate summer sea ice concentration in the marginal ice zone (MIZ) from single-channel SAR satellite imagery. They used data about melt stage, wind speed, and surface temperature to generate temporally-accumulated information, and fused this information with the SAR image, resulting in an interpretation of summer MIZ imagery. They also used the results of previous classifications of the same area to guide and correct future interpretations, thus fusing historical information with imagery and nonimagery data. They chose to study the summer MIZ since summer melt conditions cause classification based upon backscatter intensity to fail, as the backscatter of open water, thin ice, first-year ice, and multiyear ice overlap to a large degree. This makes it necessary to fuse various information and data to achieve proper segmentation and automated classification of the image. Their results were evaluated qualitatively and showed that their approach produces very good ice concentration estimates in the summer MIZ.

Documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal changes and is not subject to limitations caused by using predefined parameters.>