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Background subtraction is a technique for detecting moving objects in video frames. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. Video object extraction is a critical task in multimedia analysis and editing. Normally, the...
Conventional stereoscopic video content production requires use of dedicated stereo camera rigs which is both costly and lacking video editing flexibility. In this paper, we propose a novel approach which only requires a small number of standard cameras sparsely located around a scene to automatically convert the monocular inputs into stereoscopic streams. The approach combines a probabilistic spatio-temporal...
In this paper, a quasi-automatic video matting approach which can preserve the temporal consistency of the alpha mattes is presented. “Quasi-automatic” means that it only needs a few user interactions on the first frame. A new algorithm which incorporates the Bayesian Estimation, Weighted Kernel Density Estimation (WKDE) and graph cut is presented to automatically and accurately segment each frame...
In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground...
We propose a new solution to the problem of bi-layer video segmentation in terms of both, hardware design and algorithmic solution. At the data acquisition stage, we combine color video with infrared video, which is robust to illumination changes and provides an automatic initialization of the cue map for foreground-background segmentation. Two algorithms are presented to complete the segmentation,...
This paper proposes an automatic foreground segmentation system based on Gaussian mixture models and dynamic graph cut algorithm. An adaptive per-pixel background model is developed to set the data cost of an image graph. Shadow detection which is important to the foreground segmentation is introduced in this paper. A boundary smoothing algorithm is presented so as to eliminate edge aliasing and composite...
In this paper, we present an interactive foreground extraction tool for photo and video editing purpose. For a photo, it is firstly pre-segmented using a propagation method. Then the user can draw some strokes on the foreground objects and the background scenes to point out witch part of the photo should be extracted as an area of interest (object). A region based graph cuts algorithm is used to segment...
In this paper, we propose an approach to segment the multiple objects in video. For a video sequence with stationary background, our approach combines the feature points with the color and contrast information to extract the multiple objects of different sizes. The idea is that the local features of the feature points are more robust than that of the pixels, and more accurate than the global color...
In this work, video segmentation is viewed as an efficient intra-frame grouping temporally reinforced by a strong inter-frame coherence. Traditional approaches simply regard pixel motions as another prior in the MRF-MAP framework. Since pixel pre-grouping is inefficiently performed on every frame, the strong correlation between inter-frame groupings is largely underutilized. We exploit the inter-frame...
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