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The mesh-LBP: a framework for extracting local binary patterns from discrete manifolds.

Naoufel Werghi, Stefano Berretti, Alberto del Bimbo

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 15, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces mesh-local binary patterns (LBP) for 3D surface analysis, extending 2D image techniques to triangular meshes. Mesh-LBP demonstrates effectiveness in 3D texture classification and robustness to mesh irregularities.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Geometric Processing

    Background:

    • Local Binary Patterns (LBP) are widely used for texture analysis in 2D images.
    • Extending 2D image analysis techniques to 3D surfaces, particularly triangular meshes, remains a challenge.
    • Existing methods often struggle with the irregular nature of 3D scanned data.

    Purpose of the Study:

    • To introduce a novel framework, mesh-local binary pattern (mesh-LBP), for computing local patterns on triangular mesh surfaces.
    • To adapt and extend 2D LBP variants to the 3D mesh manifold.
    • To evaluate the effectiveness and robustness of mesh-LBP for 3D texture classification.

    Main Methods:

    • Developed a mesh-local binary pattern (mesh-LBP) framework for triangular meshes.
    • Derived mesh-LBP variants capable of extending 2D LBP techniques to 3D surfaces.
    • Conducted experiments on public datasets for 3D texture classification, including uniformity and repeatability tests.

    Main Results:

    • Demonstrated the presence of the uniformity aspect in mesh-LBP, analogous to 2D LBP.
    • Confirmed the rotation-invariance of mesh-LBP descriptors through repeatability experiments.
    • Showcased the effectiveness of mesh-LBP for 3D texture classification, outperforming state-of-the-art methods and 2D LBP on depth images.

    Conclusions:

    • The proposed mesh-LBP framework successfully extends 2D LBP concepts to 3D triangular meshes.
    • Mesh-LBP exhibits desirable properties like uniformity and rotation-invariance, crucial for robust surface analysis.
    • Mesh-LBP proves effective and robust for 3D texture classification, even with irregular mesh data typical of 3D scans.