Segmentation by grouping junctions

Hiroshi Ishikawa, Davi Geiger

Research output: Contribution to journalConference articlepeer-review


We propose a method for 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 by first finding junctions in the image and then seeking a minimum set of threshold values that preserves the junctions. Then if finds a segmentation map that maps each pixel to the level with the closest gray value to the pixel data, within a smoothness constraint. For a convex smoothing penalty, we show the global optimal solution for an energy function that fits the data can be obtained in a polynomial time, by a novel use of the maximum-flow algorithm. Our approach is in contrast to a view in computer vision where segmentation is driven by intensity gradient, usually not yielding closed boundaries.

Original languageEnglish (US)
Pages (from-to)125-131
Number of pages7
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 1998
EventProceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Santa Barbara, CA, USA
Duration: Jun 23 1998Jun 25 1998

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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