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Fast Automatic Fuzzy C-Means Knitting Pattern Color-Separation Algorithm Based on Superpixels.

Xin Ru1, Ran Chen1, Laihu Peng1

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

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|January 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated color separation algorithm for degraded knitting patterns. The novel method enhances pattern clarity, accurately separates colors, and reduces processing time.

Keywords:
blind super-resolution networkcolor-separation algorithmdensity peak clustering (DPC)fast fuzzy c-means (FCM)knitting CADsuperpixel algorithm

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Area of Science:

  • Computer Vision
  • Digital Image Processing
  • Textile Design Technology

Background:

  • Knitting CAD systems utilize complex patterns with numerous colors requiring efficient color separation.
  • Existing color separation algorithms struggle with degraded patterns and necessitate manual parameter tuning.
  • Accurate color separation is crucial for digital textile design and manufacturing.

Purpose of the Study:

  • To develop a fast, automatic color separation algorithm for degraded knitting patterns.
  • To eliminate the need for manual clustering quantity management in color separation.
  • To improve the accuracy and efficiency of color separation in knitting CAD applications.

Main Methods:

  • Utilized Real-ESRGAN for blind super-resolution to enhance degraded pattern clarity.
  • Employed an improved MMGR-WT superpixel algorithm for accurate edge segmentation.
  • Integrated an improved Density Peak Clustering (DPC) algorithm for automatic cluster number determination.
  • Applied fast Fuzzy C-Means (FCM) clustering based on color histograms for final color separation.

Main Results:

  • The proposed algorithm successfully clarifies degraded patterns and achieves high-resolution image output.
  • Automatic determination of color clusters and accurate color separation of degraded patterns were achieved.
  • The algorithm demonstrated a lower running time compared to existing methods.
  • Achieved a segmentation accuracy of 95.78% for color separation on 30 degraded patterns.

Conclusions:

  • The developed algorithm offers an effective solution for automatic and accurate color separation of degraded knitting patterns.
  • This approach significantly improves upon current methods by automating parameter selection and enhancing processing speed.
  • The findings have practical implications for improving efficiency and quality in the textile design industry.