Advertisement

Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions

  • Juan Antonio Monroy Kuhn
  • Peter Bell
  • Björn Ommer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)

Abstract

Whereas one part of art history is a history of inventions, the other part is a history of transfer, of variations and copies. Art history wants to understand the differences between these, in order to learn about artistic choices and stylistic variations. In this paper we develop a method that can detect variations between artworks and their reproductions, in particular deformations in shape. Specifically, we present a novel algorithm which automatically finds regions which share the same transformation between original and its reproduction. We do this by minimizing an energy function which measures the distortion between local transformations of the shape. Thereby, the grouping and registration problem are addressed jointly and model complexity is obtained using a stability analysis. Moreover, our method allows art historians to evaluate the exactness of a copy by identifying which contours where considered relevant to copy. The proposed shape-based approach thus helps to investigate art through the art of reproduction.

Keywords

Transformation Model Thin Plate Spline Local Transformation Landmark Point Contour Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Panofsky, E.: Renaissance and Renascences in Western Art. Harper (1960)Google Scholar
  2. 2.
    Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. Wiley (1998)Google Scholar
  3. 3.
    Monroy, A., Carque, B., Ommer, B.: Reconstructing the drawing process of reproductions from medieval images. In: ICIP (2011)Google Scholar
  4. 4.
    Wang, J., Adelson, E.: Representing moving images with layers. IEEE Trans. on IP 3(5) (1994)Google Scholar
  5. 5.
    Sýkora, D., Dingliana, J., Collins, S.: As-rigid-as-possible image registration for hand-drawn cartoon. In: NPAR (2009)Google Scholar
  6. 6.
    Chang, Y.S., Stork, D.G.: Warping realist art to ensure consitent perspective: A new software tool for art investigations. In: Human Vision and Electronic Imaging (2012)Google Scholar
  7. 7.
    Usami, Y., Stork, D.G., Fujiki, J., Hino, H., Akaho, S., Murata, N.: Improved methods for dewarping images in convex mirrors in fine art: Applications to van eyck and parmigianino. In: Computer Vision and Image Analysis of Art II (2011)Google Scholar
  8. 8.
    Maire, M., Arbelaez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural images. In: CVPR (2008)Google Scholar
  9. 9.
    Yuille, A., Kosowsky, J.: Statistical physics algorithms that converge. Neural Computation (6), 341–356 (1994)Google Scholar
  10. 10.
    Yuille, A., Rangarajan, A.: The concave-convex procedure (cccp). In: Advances in Neural Information Processing Systems 14 (2002)Google Scholar
  11. 11.
    Bezdek, J.: Pattern Recognition with fuzzy objective function algorithms. Plenum Press (1981)Google Scholar
  12. 12.
    von Luxburg, U.: Clustering stability: an overview. Foundations and Trends in Machine Learning 2(3) (2010)Google Scholar
  13. 13.
    Myronenko, A., Song, X.: Point set registration: coherent point drift. PAMI 32(12) (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Juan Antonio Monroy Kuhn
    • 1
  • Peter Bell
    • 1
    • 2
  • Björn Ommer
    • 1
  1. 1.Interdisciplinary Center for Scientific ComputingGermany
  2. 2.Institute of European Art HistoryUniversity of HeidelbergGermany

Personalised recommendations