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Updated: Mar 30, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Shortest Paths with Higher-Order Regularization.

Johannes Ulen, Petter Strandmark, Fredrik Kahl

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 6, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method for detecting thin, elongated structures in 3D data using shortest paths and higher-order curve properties. The technique efficiently identifies complex shapes with high accuracy, proving useful in medical imaging and reconstruction tasks.

    Related Experiment Videos

    Last Updated: Mar 30, 2026

    Modeling the Functional Network for Spatial Navigation in the Human Brain
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    Area of Science:

    • Computer Vision
    • Image Analysis
    • Computational Geometry

    Background:

    • Detecting thin, elongated structures in images and volumes is crucial for various applications.
    • Existing methods may struggle with complex shapes or higher-order properties like curvature and torsion.

    Purpose of the Study:

    • To present a new computational method for identifying thin, elongated structures in 2D and 3D data.
    • To incorporate higher-order curve properties (curvature, torsion) into structure detection.
    • To demonstrate the method's efficiency and practical utility.

    Main Methods:

    • Utilizes shortest path algorithms on line graphs to minimize functionals of higher-order curve properties.
    • Employs local optimization for refining detected curves to ensure smoothness.
    • Incorporates constraints on curve properties, such as maximum integrated curvature.

    Main Results:

    • Achieves efficient detection of thin, elongated structures, often in seconds on a single computer.
    • Successfully performs experiments in three dimensions with curvature and torsion regularization.
    • Handles very large graphs, with up to hundreds of billions of arcs.

    Conclusions:

    • The proposed method offers a significant advancement in detecting complex, elongated structures.
    • Higher-order regularization proves valuable and practical in 3D applications.
    • Demonstrated effectiveness in medical image analysis and multi-view reconstruction.