Segmentation by Grouping Junctions

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Segmentation by Grouping Junctions
We propose a methodfor segmenting gray-value images. By segmentation, we mean a map from the set of pixels to a small set of levels such that each connected component of the set of pixels with the same level forms a relatively large and "meaningful" region. The method finds a set of levels with associated gray values byjirstjinding junctions in the image and then seeking a minimum set of threshold values that preserves the junctions. Then it finds a segmentation map that maps each pixel to the level with the closest gray value to the pixel data, within a smoothness construint. For a convex smoothing penalty, we show the global optimal solution for an energy function that fits the data can be obtained in u polynomial time, by a novel use of the muximum-flow algorithm. Our upproach is in contrast to a view in computer vision where segmentation is driven by intensity gradient, usually not yielding closed boundaries.
Hiroshi Ishikawa 0002, Davi Geiger
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 1998
Where CVPR
Authors Hiroshi Ishikawa 0002, Davi Geiger
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