Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video matting of complex scenes
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2005 Papers
ACM SIGGRAPH 2005 Papers
Stroke Surfaces: Temporally Coherent Artistic Animations from Video
IEEE Transactions on Visualization and Computer Graphics
Video SnapCut: robust video object cutout using localized classifiers
ACM SIGGRAPH 2009 papers
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Video object cutout aims to extract homogenous objects from background in a video clip, which is a key process in many video processing fields, such as video compositing, video stylized rendering and so on. In this paper, we present a novel video cutout method by matching hierarchical structure of video frames. We first segment each frame by mean shift and construct hierarchical structure as a tree in preprocess stage. We then require user's interaction to label objects in a key frame. We further model video segmentation as matching hierarchical structure of frame and proposed an inter-frame matching algorithm. Experimental results show that our method can achieve desirable video segmentation results.