A robust approach for super resolution is presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion erros, inaccurate blur models, noise, moving objects, motion blur etc. This tobusiness is needed since super-resolution methods are very sensitive

and evaluate the models of impact and sliding. These experiments have shown that objects rotate less than predicted; we have found that the addition of a scaling factor for the torque due to friction enables the models to predict object motion reasonably well. In order to do positioning experiments, we

illumination changes and motion blur, that no-toriously make energy minimization-based tracking meth-ods get trapped in a local minimum, we propose a learning-based method that is robust to all these problems. We use random forests to learn the relation between the parameters that defines the object’s motion

motion-based video codecs. Prior work has mainly taken an approach that prevents display errors from occurring from the first place. This approach is not effective for interactive video transmission over the target network environments because data losses inevitably occur in such networks and repairing