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Principles of Image Processing and Feature Recognition Applied to Full-Field Measurements

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Special Topics in Structural Dynamics, Volume 6

Abstract

Recent advances in measurement techniques such as digital image correlation (DIC) allow full-field maps (images) of vibration shapes or strain to be obtained easily. This generally results in the acquisition of large volumes of highly redundant data. Fortunately, image decomposition offers feasible techniques for data condensation while retaining essential information. The selection, or construction, of decomposition basis (kernel) functions is essential to data reduction and has been shown to produce descriptions of the full-field image capable of accurate reproduction of the original data, very efficiently. Image descriptors are robust to measurement noise. Classical orthogonal kernel functions include Fourier series, wavelets and Legendre, Zernike and Tchebichef polynomials, defined on either rectangular or circular domains. In practice full-field measurements of the engineering components are usually non-planar within irregular domains, so that the classical kernel functions are not immediately applicable. This problem may be addressed using a methodology based on adaptive geometric moment descriptors (AGMD) as will be demonstrated in a series of illustrative examples. Model updating from full-field measurements and modal testing in the shape-feature domain are enabled with attendant advantages of the full-field data over measurements taken with a limited number of sensors at discrete locations.

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Acknowledgement

The authors wish to acknowledge the support of EC FP7 project ADVISE (Advanced Dynamic Validations using Integrated Simulation and Experimentation) -- grant no. 218595. Several figures and tables were reprinted from references [37] and [38] with permission from Elsevier. Several figures and tables were reprinted from: W. Wang and J.E. Mottershead, Adaptive moment descriptors for full-field strain and displacement measurements, Journal of Strain Analysis in Engineering Design, 48(1), 2013, 16–35 (published by SAGE)

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Authors and Affiliations

  1. School of Engineering, University of Liverpool, Liverpool, L69 3GH, UK

    John E. Mottershead

  2. School of Engineering, Manchester Metropolitan University, Manchester, M1 5GDK, UK

    Weizhuo Wang

Authors
  1. John E. Mottershead
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  2. Weizhuo Wang
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Correspondence to John E. Mottershead .

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Editors and Affiliations

  1. University of Cincinnati, Cincinnati, 45221-0018, Ohio, USA

    Randall Allemang

  2. , The Enterprise Program, Michigan Technological University, Townsend Drive 1400, Houghton, 49931, Mississippi, USA

    James De Clerck

  3. , Dept. of Mechanical Engineering, University of Massachusetts Lowell, Riverside St. 197, Lowell, 01854, Massachusetts, USA

    Christopher Niezrecki

  4. & State University, Department of Mechanical Engineering, Virginia Polytechnic Institute, Randolph Hall 114 T, Blacksburg, 24060, Virginia, USA

    Alfred Wicks

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Mottershead, J.E., Wang, W. (2013). Principles of Image Processing and Feature Recognition Applied to Full-Field Measurements. In: Allemang, R., De Clerck, J., Niezrecki, C., Wicks, A. (eds) Special Topics in Structural Dynamics, Volume 6. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6546-1_45

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  • DOI: https://doi.org/10.1007/978-1-4614-6546-1_45

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-6545-4

  • Online ISBN: 978-1-4614-6546-1

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