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Complementary Optic Flow

机译:互补光流

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摘要

We introduce the concept of complementarity between data and smoothness term in modern variational optic flow methods. First we design a sophisticated data term that incorporates HSV colour representation with higher order constancy assumptions, completely separate robust penalisation, and constraint normalisation. Our anisotropic smoothness term reduces smoothing in the data constraint direction instead of the image edge direction, while enforcing a strong filling-in effect orthogonal to it. This allows optimal complementarity between both terms and avoids undesirable interference. The high quality of our complementary optic flow (COF) approach is demonstrated by the current top ranking result at the Middlebury benchmark.
机译:我们介绍了现代变分光学流方法中数据和平滑项之间的互补性概念。首先,我们设计一个复杂的数据术语,该术语将HSV颜色表示与更高阶的恒定性假设结合在一起,将鲁棒的惩罚与约束归一化完全分开。我们的各向异性平滑项会减少在数据约束方向(而不是图像边缘方向)上的平滑,同时强制执行与其垂直的强填充效果。这允许两个项之间的最佳互补,并避免了不希望的干扰。在Middlebury基准测试中,当前排名最高的结果证明了我们互补光流(COF)方法的高质量。

著录项

  • 来源
  • 会议地点 Bonn(DE);Bonn(DE)
  • 作者单位

    Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University, Saarbruecken, Germany Max-Planck Institute for Informatics, Saarbruecken, Germany;

    rnMathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University, Saarbruecken, Germany;

    rnMathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University, Saarbruecken, Germany;

    rnMathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University, Saarbruecken, Germany;

    rnDepartamento de Informatica y Sistemas Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;

    rnInstitut fuer Informationsverarbeitung, University of Hannover Hannover, Germany;

    Max-Planck Institute for Informatics, Saarbruecke;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 信息处理(信息加工);
  • 关键词

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