Graph-Based Clustering for Apictorial Jigsaw Puzzles of Hand Shredded Content-less Pages

  • Lalitha K.S.
  • Sukhendu Das
  • Arun Menon
  • Koshy Varghese
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10127)

Abstract

Reassembling hand shredded content-less pages is a challenging task, with applications in forensics and fun games. This paper proposes an efficient iterative framework to solve apictorial jigsaw puzzles of hand shredded content-less pages, using only the shape information. The proposed framework consists of four phases. In the first phase, normalized shape features are extracted from fragment contours. Then, for all possible matches between pairs of fragments transformation parameters for alignment of fragments and three goodness scores are estimated. In the third phase, incorrect matches are eliminated based on the score values. The alignments are refined by pruning the set of pairwise matched fragments. Finally, a modified graph-based framework for agglomerative clustering is used to globally reassemble the page(s). Experimental evaluation of our proposed framework on an annotated dataset of shredded documents shows the efficiency in the reconstruction of multiple content-less pages from arbitrarily torn fragments.

Keywords

Content-less page reassembly Partial contour matching Shape features Agglomerative clustering Global reassembly 

References

  1. 1.
    Arthur, D., Vassilvitskii, S.: Worst-case and smoothed analysis of the ICP algorithm, with an application to the k-means method. In: 47th Annual IEEE Symposium on Foundations of Computer Science, pp. 153–164 (2006)Google Scholar
  2. 2.
    Castañeda, A.G., Brown, B.J., Rusinkiewicz, S., Funkhouser, T.A., Weyrich, T.: Global consistency in the automatic assembly of fragmented artefacts. In: The 12th International Symposium on Virtual Reality, Archaeology and Cultural Heritage, pp. 73–80 (2011)Google Scholar
  3. 3.
    Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica Int. J. Geogr. Inf. Geovisualization 10, 112–122 (1973)CrossRefGoogle Scholar
  4. 4.
    Freeman, H., Garder, L.: Apictorial jigsaw puzzles: the computer solution of a problem in pattern recognition. IEEE Trans. Electron. Comput. 13, 118–127 (1964)CrossRefGoogle Scholar
  5. 5.
    Goldberg, D., Malon, C., Bern, M.: A global approach to automatic solution of jigsaw puzzles. In: Eighteenth Annual Symposium on Computational Geometry, pp. 82–87 (2002)Google Scholar
  6. 6.
    Hoff, D.J., Olver, P.J.: Automatic solution of jigsaw puzzles. J. Math. Imaging Vis. 49, 234–250 (2014)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Justino, E., Oliveira, L.S., Freitas, C.: Reconstructing shredded documents through feature matching. Forensic Sci. Int. 160, 140–147 (2006)CrossRefGoogle Scholar
  8. 8.
    Kong, W., Kimia, B.B.: On Solving 2D and 3D puzzles using curve matching. In: 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 583–590 (2001)Google Scholar
  9. 9.
    Liu, H., Cao, S., Yan, S.: Automated assembly of shredded pieces from multiple photos. IEEE Trans. Multimedia 13, 1154–1162 (2011)CrossRefGoogle Scholar
  10. 10.
    Radack, G.M., Badler, N.I.: Jigsaw puzzle matching using a boundary-centered polar encoding. Comput. Graph. Image Process. 19, 1–17 (1982)CrossRefGoogle Scholar
  11. 11.
    Richter, F., Ries, C.X., Cebron, N., Lienhart, R.: Learning to reassemble shredded documents. IEEE Trans. Multimedia 15, 582–593 (2013)CrossRefGoogle Scholar
  12. 12.
    Richter, F., Ries, C.X., Romberg, S., Lienhart, R.: Partial contour matching for document pieces with content-based prior. In: 2014 IEEE International Conference on Multimedia & Expo, pp. 1–6 (2014)Google Scholar
  13. 13.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Third International Conference on 3-D Digital Imaging and Modeling, pp. 145–152 (2001)Google Scholar
  14. 14.
    Sağiroğlu, M., Erçil, A.: A texture based matching approach for automated assembly of puzzles. In: The 18th International Conference on Pattern Recognition, vol. 3, pp. 1036–1041 (2006)Google Scholar
  15. 15.
    Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)CrossRefGoogle Scholar
  16. 16.
    Stieber, A., Schneider, J., Nickolay, B., Krüger, J.: A contour matching algorithm to reconstruct ruptured documents. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) DAGM 2010. LNCS, vol. 6376, pp. 121–130. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15986-2_13 CrossRefGoogle Scholar
  17. 17.
    Tsamoura, E., Pitas, I.: Automatic color based reassembly of fragmented images and paintings. IEEE Trans. Image Process. 19, 680–690 (2010)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zhang, K., Li, X.: A graph-based optimization algorithm for fragmented image reassembly. Graph. Models 76, 484–495 (2014)CrossRefGoogle Scholar
  19. 19.
    Zhu, L., Zhou, Z., Hu, D.: Globally consistent reconstruction of ripped-up documents. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1–13 (2008)CrossRefGoogle Scholar
  20. 20.
    Zisserman, A., Forsyth, D.A., Mundy, J.L., Rothwell, C.A.: Recognizing general curved objects efficiently. In: Geometric Invariance in Computer Vision, pp. 228–251 (1992)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lalitha K.S.
    • 1
  • Sukhendu Das
    • 1
  • Arun Menon
    • 2
  • Koshy Varghese
    • 2
  1. 1.Department of Computer Science and EngineeringIIT MadrasChennaiIndia
  2. 2.Department of Civil EngineeringIIT MadrasChennaiIndia

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