In order to complement subjective evaluation of the quality of segmentation masks, this paper introduces a procedure for automatically assessing this quality. Algorithmically computed figures of merit are proposed. Assuming the existence of a perfect reference mask (ground truth), generated manually or with a reliable procedure over a test set, these figures of merit take into account visually desirable properties of a segmentation mask in order to provide the user with metrics that best quantify the spatial and temporal accuracy of the segmentation masks. For the sake of easy interpretation, results are presented on a peaked signal-to-noise ratio-like logarithmic scale.

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We present a system capable of recommending music playlists that take into account the temporal context of the user, i.e. they select user preferences as learned for the concrete time situation of the request.

Most current multiplayer 3D games can only be played on dedicated platforms, requiring specifically designed content and communication over a predefined network. To overcome these limitations, the OLGA (On-Line GAming) consortium has devised a framework to develop distributive, multiplayer 3D games. Scalability at the level of content, platforms and networks is exploited to achieve the best trade-offs between complexity and quality.