Segmentation by grouping junctions

Hiroshi Ishikawa, Davi Geiger

研究成果: Conference article

103 引用 (Scopus)

抜粋

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.

元の言語English
ページ(範囲)125-131
ページ数7
ジャーナルProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
出版物ステータスPublished - 1998 12 1
イベントProceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Santa Barbara, CA, USA
継続期間: 1998 6 231998 6 25

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ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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