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Automatic registration and mosaicking of technical images of Old Master paintings

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Abstract

The registration of technical art conservation images of Old Master paintings presents unique challenges. Specifically, X-radiographs and reflective infrared (1000–2500 nm) images reveal shifted, or new, compositional elements not visible on the surface of artworks. Here, we describe a new multimodal registration and mosaicking algorithm that is capable of providing accurate alignment of a variety of types of images, such as the registration of multispectral reflective infrared images, X-radiographs, hyperspectral image cubes, and X-ray fluorescence image cubes to reference color images taken at high spatial sampling (300–500 pixels per inch), even when content differences are present, and a validation study has been performed to quantify the algorithm’s accuracy. Key to the algorithm’s success is the use of subsets of wavelet images to select control points and a novel method for filtering candidate control-point pairs. The algorithm has been used to register more than 100 paintings at the National Gallery of Art, D.C. and The Art Institute of Chicago. Many of the resulting registered datasets have been published in online catalogues, providing scholars additional information to further their understanding of the paintings and the working methods of the artists who painted them.

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Notes

  1. 1.

    Personal communication with R. Erdmann, 2013.

  2. 2.

    A phase image is produced by computing the 2-D Fourier transform of an image then computing the inverse Fourier transform of the result using only the phase information (i.e., using unit magnitude) [20].

  3. 3.

    While the matching is performed using phase images, Fig. 5a uses the color image and IR image to show more clearly the control points over their corresponding regions.

  4. 4.

    The vertical pixel disparity is also computed, and analyzed, in the same manner as the horizontal pixel disparity.

  5. 5.

    X-radiographs are scanned at 500 pixels per inch, and thus the increase in disparity corresponds to a decrease in pixel size relative to the IR datasets.

  6. 6.

    IR image frames are generally registered at a spatial sampling near 280 pixels per inch, so the maximum shift in that case would be closer to 0.36 pixels.

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Acknowledgments

The authors acknowledge funding from the National Science Foundation (award 1041827). J.K.D. and D.M.C. acknowledge funding from the Andrew W. Mellon and Samuel H. Kress Foundations, and D.M.C. acknowledges funding from the ARCS Foundation. The authors are grateful to Kimberly Schenk, Elizabeth Walmsley, Catherine Metzger, Melanie Gifford, Barbara Berrie, and Mervin Richards of the National Gallery of Art (Washington, D.C.), and Kelly Keegan of the Art Institute of Chicago.

Conflict of interest

The authors declare that they have no conflicts of interest.

Author information

Correspondence to John K. Delaney or Murray H. Loew.

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Conover, D.M., Delaney, J.K. & Loew, M.H. Automatic registration and mosaicking of technical images of Old Master paintings. Appl. Phys. A 119, 1567–1575 (2015) doi:10.1007/s00339-015-9140-1

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Keywords

  • Color Image
  • Fiducial Marker
  • Template Image
  • Bicubic Interpolation
  • Image Cube