Higher-order gradient descent by fusion-move graph cut

研究成果: Conference contribution

22 被引用数 (Scopus)

抄録

Markov Random Field is now ubiquitous in many formulations of various vision problems. Recently, optimization of higher-order potentials became practical using higher-order graph cuts: the combination of i) the fusion move algorithm, ii) the reduction of higher-order binary energy minimization to first-order, and iii) the QPBO algorithm. In the fusion move, it is crucial for the success and efficiency of the optimization to provide proposals that fits the energies being optimized. For higher-order energies, it is even more so because they have richer class of null potentials. In this paper, we focus on the efficiency of the higher-order graph cuts and present a simple technique for generating proposal labelings that makes the algorithm much more efficient, which we empirically show using examples in stereo and image denoising.

本文言語English
ホスト出版物のタイトル2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
ページ568-574
ページ数7
DOI
出版ステータスPublished - 2009 12 1
外部発表はい
イベント12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
継続期間: 2009 9 292009 10 2

出版物シリーズ

名前Proceedings of the IEEE International Conference on Computer Vision

Conference

Conference12th International Conference on Computer Vision, ICCV 2009
CountryJapan
CityKyoto
Period09/9/2909/10/2

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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