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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Ridge network detection in crumpled paper via graph density maximization.

Chiou-Ting Hsu, Marvin Huang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 4, 2012
    PubMed
    Summary

    Automating ridge network detection in crumpled paper is challenging. This study models ridge networks as weighted graphs, using graph density optimization for effective automated detection.

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    Area of Science:

    • Physics
    • Materials Science
    • Computational Geometry

    Background:

    • Crumpled paper forms complex ridge networks, posing challenges for automated detection due to structural complexity and measurement distortions.
    • Existing methods struggle with the intricate nature of these ridge networks, necessitating novel approaches for accurate identification.

    Discussion:

    • The proposed method models ridge networks as weighted graphs, transforming detection into a graph density optimization problem.
    • A complete graph is constructed by detecting nodes and defining edge weights, followed by identifying a subgraph with maximal graph density.
    • The graph density criterion is refined by incorporating pairwise connectivity to enhance the detected ridge network's coherence.

    Key Insights:

    • Automated ridge network detection in crumpled paper is achieved through a novel graph-based optimization approach.

    Related Experiment Videos

    Last Updated: May 20, 2026

    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

  • The method effectively identifies ridge networks by maximizing graph density, overcoming limitations of prior techniques.
  • Refining the density criterion with pairwise connectivity improves the continuity and accuracy of the detected structures.

    • Outlook:

    • This graph-based approach offers a robust framework for analyzing complex topological structures in disordered materials.
    • Future work could extend this methodology to other scientific domains requiring the analysis of network-like patterns.
    • Further research may explore variations in graph construction and density criteria for enhanced performance across different crumpled materials.