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Optimized method for segmentation of ancient mural images based on superpixel algorithm.

Jinxing Liang1,2,3, Anping Liu1, Jing Zhou1

  • 1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Hubei, China.

Frontiers in Neuroscience
|November 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized superpixel-based method for segmenting ancient mural images. The novel approach enhances digital restoration by improving segmentation accuracy on complex, colorful artworks.

Keywords:
SLICancient muralsdensity-based clusteringimage segmentationk-means clusteringsuperpixel

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Area of Science:

  • Digital image processing
  • Cultural heritage preservation
  • Computer vision

Background:

  • High-precision segmentation of ancient murals is crucial for digital restoration.
  • Traditional algorithms struggle with the complex color appearance of murals.
  • Existing methods lack sufficient accuracy for detailed mural analysis.

Purpose of the Study:

  • To develop an optimized superpixel-based method for accurate ancient mural image segmentation.
  • To address the limitations of traditional algorithms in segmenting complex mural imagery.
  • To enhance the foundation for digital virtual restoration of ancient murals.

Main Methods:

  • Applied Simple Linear Iterative Clustering (SLIC) to generate superpixels.
  • Utilized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for initial superpixel clustering.
  • Implemented sequential optimization strategies including noise merging and color-based region merging.

Main Results:

  • The proposed method achieved high-precision segmentation on simulated and real mural images.
  • The optimized approach effectively handled complex color appearances and noise.
  • Comparative analysis demonstrated superior performance over existing segmentation methods.

Conclusions:

  • The novel superpixel-based method significantly improves ancient mural image segmentation accuracy.
  • This technique provides a robust foundation for the digital virtual restoration of cultural heritage.
  • The optimized approach offers a more effective solution for complex image segmentation tasks.