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IJON
2010

Inference and parameter estimation on hierarchical belief networks for image segmentation

8 years 8 months ago
Inference and parameter estimation on hierarchical belief networks for image segmentation
We introduce a new causal hierarchical belief network for image segmentation. Contrary to classical tree structured (or pyramidal) models, the factor graph of the network contains cycles. Each level of the hierarchical structure features the same number of sites as the base level and each site on a given level has several neighbors on the parent level. Compared to tree structured models, the (spatial) random process on the base level of the model is stationary which avoids known drawbacks, namely visual artifacts in the segmented image. We propose different parameterizations of the conditional probability distributions governing the transitions between the image levels. A parametric distribution depending on a single parameter allows the design of a fast inference algorithm on graph cuts, whereas for arbitrary distributions, we propose inference with loopy belief propagation. The method is evaluated on scanned document images from the 18th century, showing an improvement of character...
Christian Wolf, Gérald Gavin
Added 28 Jan 2011
Updated 28 Jan 2011
Type Journal
Year 2010
Where IJON
Authors Christian Wolf, Gérald Gavin
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