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| META TOPICPARENT |
name="Robuddies" |
Video matting, or layer extraction, is a classic inverse problem
in computer vision that involves the extraction of foreground
objects, and the alpha mattes that describe their
opacity, from a set of images. Modern approaches that
work with natural backgrounds often require user-labelled
“trimaps” that segment each image into foreground, background
and unknown regions. For long sequences, the production
of accurate trimaps can be time consuming. In contrast,
another class of approach depends on automatic background
extraction to automate the process, but existing techniques
do not make use of spatiotemporal consistency, and
cannot take account of operator hints such as trimaps.
This paper presents a method inspired by natural image
statistics that cleanly unifies these approaches. A prior is
learnt that models the relationship between the spatiotemporal
gradients in the image sequence and those in the alpha
mattes. This is used in combination with a learnt foreground
colour model and a prior on the alpha distribution to help
regularize the solution and greatly improve the automatic
performance of such systems.
The system is applied to several real image sequences
that demonstrate the advantage that the unified approach can
afford.
-- Main.simra - 26 Sep 2005 |
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