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快速自动模糊C-意味着针织图案基于超像素的颜色分离算法.

Xin Ru1, Ran Chen1, Laihu Peng1

  • 1College of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.

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PubMed
概括
此摘要是机器生成的。

这项研究引入了一种自动化颜色分离算法,用于退化的针织图案. 这种新的方法提高了图案的清晰度,准确地分离了颜色,并减少了处理时间.

关键词:
盲人超分辨率网络的网络.颜色分离算法的算法密度峰值聚类 (DPC) 是一种密度峰值聚类.快速模糊的C-平均值 (FCM)编织CAD编织 CAD编织超像素算法 超像素算法

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科学领域:

  • 计算机视觉 计算机视觉
  • 数字图像处理 数字图像处理
  • 织设计技术 织设计技术

背景情况:

  • 针织CAD系统使用复杂的图案,多种颜色,需要高效的颜色分离.
  • 现有的色彩分离算法与退化模式作斗争,需要手动参数调整.
  • 准确的颜色分离对于数字织品设计和制造至关重要.

研究的目的:

  • 开发一种快速,自动的颜色分离算法,用于退化的针织图案.
  • 为了消除在颜色分离中需要手动集群数量管理的需要.
  • 为了提高编织CAD应用中的颜色分离的准确性和效率.

主要方法:

  • 使用Real-ESRGAN用于盲目的超分辨率,以增强退化的图案清晰度.
  • 采用了改进的MMGR-WT超像素算法来实现精确的边缘细分.
  • 集成了一个改进的密度峰集群 (DPC) 算法,用于自动确定集群号.
  • 应用了基于颜色直方图的快速模糊C-Means (FCM) 聚类,用于最终的颜色分离.

主要成果:

  • 拟议的算法成功地澄清了退化的模式,并实现了高分辨率的图像输出.
  • 实现了颜色集群的自动确定和退化模式的准确色彩分离.
  • 与现有方法相比,该算法的运行时间较短.
  • 在30个退化模式上实现了95.78%的分段精度,用于分离30种退化模式的颜色.

结论:

  • 开发的算法提供了一个有效的解决方案,用于自动和准确的色彩分离退化的针织图案.
  • 这种方法通过自动化参数选择和提高处理速度,显著改善了当前的方法.
  • 这些发现对于提高织设计行业的效率和质量具有实际意义.