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Stereoscopic segmentation
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DOI：10.1023/A:1023079624234.png)
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摘要
En 中文
We cast the problem of multiframe stereo reconstruction of a smooth surface as the global region segmentation of a collection of images of the scene. Dually, the problem of segmenting multiple calibrated images of an object becomes that of estimating the solid shape that gives rise to such images. We assume that the radiance of the scene results in piecewise homogeneous image statistics. This simplifying assumption covers Lambertian scenes with constant albedo as well as fine homogeneous textures, which are known challenges to stereo algorithms based on local correspondence. We pose the segmentation problem within a variational framework, and use fast level set methods to find the optimal solution numerically. Our algorithm does not work in the presence of strong photometric features, where traditional reconstruction algorithms do. It enjoys significant robustness to noise under the assumptions it is designed for.
Keyword:
variational methods
Mumford-Shah functional
image segmentation
multi-frame stereo reconstruction
level set methods
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