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"拙"现象和计算机视觉方法来量化它.

Shijian Wu1, Mark Mintel1,2, Baran Teoman1

  • 1Colgate-Palmolive Company, Piscataway, NJ 08854, USA.

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PubMed
概括
此摘要是机器生成的。

我们使用Python开发了一种快速,负担得起的计算机视觉方法,用于测量凝和液体中的"粘度". 这种基于喷射流破裂时间的技术与人类的感知很好地相关,与传统的风湿学不同.

关键词:
计算机视觉 计算机视觉一个的小子.Python 是一个 Python 语言.类风病学 类风病学 类风病学破裂时间 破裂时间

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科学领域:

  • 材料科学 材料科学 材料科学
  • 流体动力学 流体动力学
  • 计算机视觉 计算机视觉

背景情况:

  • 复杂的液体和凝中的""现象很普遍,但很难客观地量化.
  • 传统的风湿学测量显示,与感知到的""的相关性有限.

研究的目的:

  • 开发一种快速,具有成本效益的计算机视觉方法,用于量化复杂液体和凝中的"粘性".
  • 评估拟议方法与人类对"拙"的看法之间的相关性.

主要方法:

  • 实现了一个基于Python的计算机视觉方法.
  • 在实验室生产的淋浴凝样本中,喷射流破裂时间被用作测量"粘度"的关键指标.
  • 结果与技术洞察力小组成员团队的数据相关联.

主要成果:

  • 计算机视觉技术表明,与感知到的"愚蠢"水平有显著的相关性.
  • 来自商业剪切类风湿计的风病学测量没有显示出与""的强烈相关性.

结论:

  • 基于Python的计算机视觉技术利用喷射流提供了一个有前途的,高效的,负担得起的解决方案来评估"拙".
  • 该方法为行业提供了一种有价值的工具,用于评估液体和凝产品.