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With multi-angle video contents available at a user terminal, the user can select his/her preferred alternate views among the given multiple video streams captured at different view angles for a same event. This enhanced experience often entails streaming problems in real-time over the network. In order to cope with this problem, multi-angle video contents are encoded at different bit rates and the appropriate video streams are then selected or transcoded for delivery to meet such bandwidth constraints. Therefore, the selection and transcoding operations become essential processing steps to adapt to the time-varying bandwidth of the network. In this paper, we propose an optimal adaptation framework for streaming multiple video streams by jointly formulating the selection and transcoding problems into a unified optimization problem. Index Terms--Adaptation, multi-angle video, optimization problem, separable programming.

Linear and nonlinear adaptive networks are used for target discrimination in synthetic aperture radar imagery. Problems associated with the two-parameter constant false alarm rate detector led to the use of the quadratic gamma discriminator (QGD) as a classifier. This classifier is further improved in terms of linear processing as a multilayer perceptron (MLP) topology called the nonlinear QGD (NL-QGD).

The new MPEG-4 video coding standard enables content-based functionalities. In order to support the philosophy of the MPEG-4 visual standard, each frame of video sequences should be represented in terms of video object planes (VOP's). In other words, video objects to be encoded in still pictures or video sequences should be prepared before the encoding process starts. Therefore, it requires a prior decomposition of sequences into VOP's so that each VOP represents a moving object. This paper addresses an image segmentation method for separating moving objects from the background in image sequences. The proposed method utilizes the following spatio-temporal information. 1) For localization of moving objects in the image sequence, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed by comparing two variance estimates from two consecutive difference images, which results in an F-test. 2) Spatial segmentation is performed to divide each image into semantic regions and to find precise object boundaries of the moving objects. The temporal segmentation yields a change detection mask that indicates moving areas (foreground) and nonmoving areas (background), and spatial segmentation produces spatial segmentation masks. A combination of the spatial and temporal segmentation masks produces VOP's faithfully. This paper presents various experimental results. Index Terms-- Object segmentation, change detection mask, temporal segmentation, spatial segmentation, and tracking.