Solving jigsaw puzzle with symbol matrixes

This paper presents a new symbol-matrix-based jigsaw-puzzle algorithm for image reconstruction. The proposed algorithm first calculates the compatibility metric using the SSD (Sum of Squared Distance Scoring) between adjacent pieces. Then the algorithm constructs a matrix to express the location relationship of pieces followed by constructing a symbol matrix to record the number and rotations of pieces. Finally, we use a greed algorithm to reconstruct the images. The proposed algorithm does not require any preset conditions and can reconstruct the images rapidly. The experimental results have shown that the proposed algorithm can accurately reconstruct the images with 28% speed-up in execution time. The results also show that it's very effective to reconstruct the puzzles with missing pieces, which is a useful feature for applications such as artifact reconstruction, biological information reconstruction and incomplete crime-scene reconstruction.

[1]  Yang Wang,et al.  Robust Solvers for Square Jigsaw Puzzles , 2013, 2013 International Conference on Computer and Robot Vision.

[2]  Nathan S. Netanyahu,et al.  A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Naif Alajlan,et al.  Solving Square Jigsaw Puzzles Using Dynamic Programming and the Hungarian Procedure , 2009 .

[4]  Ayellet Tal,et al.  Solving multiple square jigsaw puzzles with missing pieces , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Andrew C. Gallagher Jigsaw puzzles with pieces of unknown orientation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Ohad Ben-Shahar,et al.  A fully automated greedy square jigsaw puzzle solver , 2011, CVPR 2011.

[7]  Longin Jan Latecki,et al.  Particle filter with state permutations for solving image jigsaw puzzles , 2011, CVPR 2011.

[8]  Ho-Jin Choi,et al.  Jigsaw puzzle image retrieval via pairwise compatibility measurement , 2014, 2014 International Conference on Big Data and Smart Computing (BIGCOMP).