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Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts,Theory and Applications

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TLDR
In this paper, a comprehensive overview of image correlation for shape, motion and deformation measurements is provided. But, the authors do not discuss the effect of out-of-plane motion on 2D measurements.
Abstract
Image Correlation for Shape, Motion and Deformation Measurements provides a comprehensive overview of data extraction through image analysis. Readers will find and in-depth look into various single- and multi-camera models (2D-DIC and 3D-DIC), two- and three-dimensional computer vision, and volumetric digital image correlation (VDIC). Fundamentals of accurate image matching are described, along with presentations of both new methods for quantitative error estimates in correlation-based motion measurements, and the effect of out-of-plane motion on 2D measurements. Thorough appendices offer descriptions of continuum mechanics formulations, methods for local surface strain estimation and non-linear optimization, as well as terminology in statistics and probability. With equal treatment of computer vision fundamentals and techniques for practical applications, this volume is both a reference for academic and industry-based researchers and engineers, as well as a valuable companion text for appropriate vision-based educational offerings.

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Journal ArticleDOI

Full-automatic seed point selection and initialization for digital image correlation robust to large rotation and deformation

TL;DR: An efficient, robust and full-automatic initial guess approach combining the speeded-up robust features (SURF) algorithm and the reliability-guided displacement tracking (RGDT) strategy is proposed, which can not only automatically select and update seed point, but also can effectively deal with target images with large deformation and rotation.
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An experimental evidence of the failure of Cauchy elasticity for the overall modeling of a non-centro-symmetric lattice under static loading

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