Video matting via opacity propagation

Tang, Zhen; Zhang, Miao; Wan, Yuanyuan; Zhang, Dianyong

doi:10.1007/s00371-011-0598-3

Cited by 16 publications

(11 citation statements)

References 11 publications

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“…They differ by the way they relate the matte and the flow. Opacity Propagation algorithm [10] uses a slanted, flow-aligned 3D window. The algorithm [11] re-weights the 3D kernel according to the optical flow.…”

Section: Related Workmentioning

confidence: 99%

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Alpha-Flow for Video Matting

Sindeev

Konushin

Rother

2013

Computer Vision – ACCV 2012

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Abstract. This work addresses the problem of video matting, that is extracting the opacity-layer of a foreground object from a video sequence. We introduce the notion of alpha-flow which corresponds to the flow in the opacity layer. The idea is derived from the process of rotoscoping, where a user-supplied object mask is smoothly interpolated between keyframes while preserving its correspondence with the underlying image. Our key contribution is an algorithm which infers both the opacity masks and the alpha-flow in an efficient and unified manner. We embed our algorithm in an interactive video matting system where the first and last frame of a sequence are given as keyframes, and additional user strokes may be provided in intermediate frames. We show high quality results on various challenging sequences, and give a detailed comparison to competing techniques.

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Section: Related Workmentioning

confidence: 99%

“…Hence, in the first iteration of our algorithm they will most likely be matched to pixels of the same color. We assume that these matched pixels often have the same alpha value (similar assumption is made in [10] when they match image windows using SSD metric). Note, this assumption is not always true.…”

Section: Flow Computationmentioning

confidence: 99%

Alpha-Flow for Video Matting

Sindeev

Konushin

Rother

2013

Computer Vision – ACCV 2012

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“…Tong et al [15] designed a novel interface which can efficiently select and cut out video objects as a video plays. Tang et al [16] proposed a novel video matting method based on opacity propagation. All these video matting approaches rely on having a well-defined object boundary in the source video.…”

Section: Related Workmentioning

confidence: 99%

Motion-Aware Gradient Domain Video Composition

Chen

Zhu

Shamir

et al. 2013

IEEE Trans. on Image Process.

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Abstract-For images, gradient domain composition methods like Poisson blending offer practical solutions for uncertain object boundaries and differences in illumination conditions. However, adapting Poisson image blending to video faces new challenges due to the added temporal dimension. In video, the human eye is sensitive to small changes in blending boundaries across frames, and slight differences in motions of the source patch and target video. We present a novel video blending approach that tackles these problems by merging the gradient of source and target videos and optimizing a consistent blending boundary based on a user provided blending trimap for the source video. Our approach extends mean-value coordinates interpolation to support hybrid blending with a dynamic boundary while maintaining interactive performance. We also provide a user interface and source object positioning method that can efficiently deal with complex video sequences beyond the capabilities of alpha blending.

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“…1, when compared with global classifiers, local classifiers are more sensitive to temporal discontinuities because of their limited coverage. In fact, the problem is not restricted to the local classifier, all methods relying on local temporal continuity suffer from the same problem, including the 3D graph-cut [Li et al 2005;Tong et al 2011] and the 3D extension [Tang et al 2011] of the color line model [Levin et al 2008] in video cube.…”

Section: Introductionmentioning

confidence: 99%

Discontinuity-aware video object cutout

et al. 2012

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Existing video object cutout systems can only deal with limited cases. They usually require detailed user interactions to segment real-life videos, which often suffer from both inseparable statistics (similar appearance between foreground and background) and temporal discontinuities (e.g. large movements, newly-exposed regions following disocclusion or topology change).In this paper, we present an efficient video cutout system to meet this challenge. A novel directional classifier is proposed to handle temporal discontinuities robustly, and then multiple classifiers are incorporated to cover a variety of cases. The outputs of these classifiers are integrated via another classifier, which is learnt from real examples. The foreground matte is solved by a coherent matting procedure, and remaining errors can be removed easily by additive spatio-temporal local editing. Experiments demonstrate that our system performs more robustly and more intelligently than existing systems in dealing with various input types, thus saving a lot of user labor and time.

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Video matting via opacity propagation

Cited by 16 publications

References 11 publications

Alpha-Flow for Video Matting

Alpha-Flow for Video Matting

Motion-Aware Gradient Domain Video Composition

Discontinuity-aware video object cutout

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