Abstract
The proposed technique deals with jigsaw puzzles and takes advantage of both geometrical and color features. It is considered that an image is being divided into pieces. The shape of these pieces is not predefined, yet the background’s color is. The whole method concerns a recurrent algorithm, which initially, finds the most important corner points around the contour of a piece, afterwards performs color segmentation with a Kohonen’s SOFM based technique and finally uses a comparing routine. This routine is based on the corner points found before. It compares a set of angles, the color of the image around the region of the corner points, the color of the contour and finally compares sequences of points by calculating the Euclidean distance of luminance between them. At a final stage the method decides which pieces match. If the result is not satisfying, the algorithm is being repeated with new adaptive modified parameter values as far as the corner points and the color segmentation is concerned.
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References
Freeman, H., Gardner, L.: Apictorial jigsaw puzzles: The computer solution of a problem in pattern recognition. IEEE Trans. on Electronic Computers 13, 118–127 (1964)
Kong, W., Kimia, B.B.: On solving 2D and 3D puzzles using curve matching. In: Proc. IEEE Computer Vision and Pattern Recognition (2001)
da Gama Leitao, H.C., Stolfi, J.: Automatic reassembly of irregular fragments. Tech. Report IC-98-06, Univ. of Campinas (1998)
da Gama Leitao, H.C., Stolfi, J.: Information Contents of Fracture Lines. Tech. Report IC-99-24, Univ. of Campinas (1999)
Leutwyle, K.: Solving a digital jigsaw puzzle, http://www.sciam.com/explorations/2001/062501fresco/
Levoy, M.: The digital Forma Urbis Romae project, http://www.graphics.stanford.edu/projects/forma-urbis/
Wang, C.: Determining the Molecular Conformation from Distance or Density Data. PhD thesis, Department of Electrical Engineering and Computer Science, MIT (2000)
Papamarkos, N., Atsalakis, A., Strouthopoulos, C.: Adaptive Color Reduction. IEEE Trans. on Systems, Man, and Cybernetics-Part B 32(1), 44–56 (2002)
Papamarkos, N.: Color reduction using local features and a SOFM neural network. Int. Journal of Imaging Systems and Technology 10(5), 404–409 (1999)
Atsalakis, A., Papamarkos, N., Kroupis, N., Soudris, D., Thanailakis, A.: A Color Quantization Technique Based On Image Decomposition and Its Hardware Implementation. IEE Proceedings Vision, Image and Signal Processing 151, 511–524 (2004)
Wolfson, H.: On curve matching. PAMI 12, 483–489 (1990)
Chetverikov, D.: Zs. Szabo. A Simple and E_cient Algorithm for Detection of High Curvature Points in Planar Curves. In: M. Vincze, editor, Robust Vision for Industrial Applications, volume 128 of Schriftenreihe der Osterreihicshen Computer Gesellschaft, pp. 175,184, Steyr, Austria, Osterreihicshe Computer Gesellschaft (1999)
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Makridis, M., Papamarkos, N., Chamzas, C. (2005). An Innovative Algorithm for Solving Jigsaw Puzzles Using Geometrical and Color Features. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_99
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DOI: https://doi.org/10.1007/11578079_99
Publisher Name: Springer, Berlin, Heidelberg
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