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Topographic Surveying and Contours

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Related Experiment Video

Updated: May 9, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

Incorporating patch subspace model in Mumford-Shah type active contours.

Junyan Wang, Kap Luk Chan

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 30, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new energy minimization model for segmenting non-smooth image textures. The method improves segmentation accuracy by optimizing linear patch reconstruction, offering superior performance in unsupervised texture segmentation.

    Related Experiment Videos

    Last Updated: May 9, 2026

    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
    14:14

    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

    Published on: April 16, 2017

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Segmentation of non-smooth image structures like textures is challenging.
    • Existing methods often rely on predefined texture features, limiting their generalizability.

    Purpose of the Study:

    • To propose a unified energy minimization model for unsupervised segmentation of non-smooth image structures.
    • To establish a computational framework based on patch subspaces and linear patch models.

    Main Methods:

    • Developed a model based on the Mumford-Shah functional and a linear patch model.
    • Formulated segmentation as minimizing a piecewise linear patch reconstruction energy.
    • Derived an algorithm for globally optimal linear patch reconstruction with linear convergence.

    Main Results:

    • Segmentation error is bounded by the linear patch reconstruction error.
    • The method achieves unsupervised segmentation without predefined features.
    • Generated optimized orthonormal descriptors for segmented regions.

    Conclusions:

    • The proposed method offers superior performance for general texture segmentation compared to existing approaches.
    • Improving linear patch reconstruction directly reduces segmentation error.
    • The model is suitable for handling diverse textures in an unsupervised manner.