抄録
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.
本文言語 | English |
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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 |
出版ステータス | Published - 1998 12月 1 |
外部発表 | はい |
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月 23 → 1998 6月 25 |
ASJC Scopus subject areas
- ソフトウェア
- コンピュータ ビジョンおよびパターン認識