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DAGM
2006
Springer

Robust Variational Segmentation of 3D Objects from Multiple Views

10 years 6 months ago
Robust Variational Segmentation of 3D Objects from Multiple Views
We propose a probabilistic formulation of 3D segmentation given a series of images from calibrated cameras. Instead of segmenting each image separately in order to build a 3D surface consistent with these segmentations, we compute the most probable surface that gives rise to the images. Additionally, our method can reconstruct the mean intensity and variance of the extracted object and background. Although it is designed for scenes, where the objects can be distinguished visually from the background (i.e. images of piecewise homogeneous regions), the proposed algorithm can also cope with noisy data. We carry out the numerical implementation in the level set framework. Our experiments on synthetic data sets reveal favorable results compared to state-of-the-art methods, in particular in terms of robustness to noise and initialization.
Kalin Kolev, Thomas Brox, Daniel Cremers
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where DAGM
Authors Kalin Kolev, Thomas Brox, Daniel Cremers
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