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Stereo-rig Design: Camera Selection—Part 2

Authors

  • Phillip Reu


  • The Art and Application of Digital Image Correlation is written by Phillip L. Reu. He received his PhD from the University of Wisconsin–Madison and is currently a Principal Member of Technical Staff at Sandia National Laboratory. He began working with digital image correlation in 2004 and is focused on understanding the influence of the unavoidable compromises made in field measurements on the final DIC uncertainty. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under contract No. DE-AC04-94AL85000.
    Email:
    Phillip.Reu.DIC@gmail.com

  • This is obviously an evolving area. The camera technology has been improving at an incredible rate.

  • Detector size is from the video tube days and is not reflective of the actual detector size.

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  • There are other larger format lens mounts but they are very rare in digital imaging. Also, I have reservations about whether using a 14 megapixel camera at laboratory scale fields-of-view (inches or centimeters square) yields the resolution scaling one would expect.

    Comparison of DIC results of two different images, one with low contrast and high noise (high gain) and the other with better contrast and lower noise. Note the difference in displacement noise in the static pre-test images shown on the bottom (identical scales).

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  • This statement is a moving target.

    Exercise for the Reader

    The effect of noise on DIC can be investigated using a 2D DIC system. Acquire two static images and then create an exact copy of the reference image file and rename it. Run the correlation on the three images; the reference, the copied reference, and an independent static image. The DIC will show a perfect match for the copy while showing errors for the “identical” but independently obtained static images. Why? The camera image noise is different between the two independent images and creates both a bias error and a variance error in the match. The copied image has the exact same “noise” as the reference image and the “correlated” noise appears like a speckle and results in a perfect match.

No abstract is available for this article.

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