摘要
在线视频分割的应用通常都需要对分割结果进行后处理,以消除误分割和边沿闪烁.基于图像抠图的方法太慢,而简单的对(前/背景)边界进行模糊不仅不能消除误分割,而且会导致清晰的边界被过度模糊.为了解决上述问题,文中提出了一种新的后处理方法.对边界附近的每一像素,首先通过一种新的专门用于颜色聚类的快速聚类算法得到该像素周围的局部颜色模型,并用来重新估计像素的alpha值,以消除误分割.为了改善结果的一致性,再采用一种自适应的边界函数作光滑性约束.边界函数可根据像素的局部属性自适应地调整过渡区域的中心和宽度,这样就防止了将清晰的边界过度模糊.文中的算法速度很快,可以很好地满足在线视频分割的要求.
Applications of online video segmentation usually need to do post-processing in order to remove mis-segmentation and suppress flicking. Traditional matting-based methods are too slow, while simply blur the （foreground/background） boundary not only cause over-blur but also can＇t remove mis-segmentation. This paper proposes a novel post-processing method. For each pixel around the boundary, a local color model is first estimated through a new fast clustering algorithm, which is designed specially for color clustering. Mis-segmentation is then removed by re-estimating the alpha value for each pixel according to its local color model. In order to improve the consistence of result, an adaptive edge model is applied as a smooth constraint. The edge model can adjust the center and width of the transition region according to the local context of each pixel, and this way prevents the boundary from being over-blurred. The proposed method is very fast, and can meet the requirement of online video segmentation very well.
出处
《计算机学报》
EI
CSCD
北大核心
2009年第2期261-267,共7页
Chinese Journal of Computers
基金
国家“九七三”重点基础研究发展规划项目基金(2002CB312101)
国家“八六三”高技术研究发展计划项目基金(2007AA01Z326)资助~~
