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We present a multi-feature based object extraction algorithm in this paper. Cutting in from the angle of multi-feature, this algorithm combines the high-efficient and good performance Graph Cut framework. We add foreground shape information and motion estimation as a compensation of the instability of using single color information.
This paper proposes background segmentation methods that integrate color, Ibp contrast and motion cues so as to improve the segmentation results. The background color models based on GMMs are developed to describe the scene. Motion cues is presented to detect the legitimate moved foreground pixels combining with the background subtraction. Lbp contrast features are used to calculate the image contrast...
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...
This paper addresses the problem of natural image segmentation. We develop the interactive image segmentation system and construct two-scale graphs, including region-based graph and pixel-level graph. Building region-level graph is to partition the image into several constituent components. Graph cut is used to extract the foreground object from the image. New data cost functions are defined in the...
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