This paper presents a robust algorithm for video sequences stabilization. Motion estimation is achieved using block motion vectors. In this way the same motion estimator of mpeg encoder can be used. The simple use of block motion vectors can give unreliable global motion vectors and so elaborations are done to make the algorithm robust.
The paper describes a system aimed at improving the human machine interaction that is able to identify users according how she looks at the monitor. The proposed system does not need invasive measurements that could limit the naturalness of her actions. The approach, here described, detects the gaze movements on the monitor and clusters the sequences of user gaze fixation points on the screen characterizing the user according the particular patterns her gaze follows. The recognition of the user is performed through a clustering process employing the Mean-Shift algorithm and it can open new perspective in human-machine interaction. In particular, the parameters of the clustering process are tuned optimizing an entropy oriented cost function that allows an automatic selection of the best parameters setting.
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