Solving jigsaw puzzles by computer
Annals of Operations Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
A global approach to automatic solution of jigsaw puzzles
Proceedings of the eighteenth annual symposium on Computational geometry
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Digital Image Processing
A shape and image merging technique to solve jigsaw puzzles
Pattern Recognition Letters
Determining molecular conformation from distance or density data
Determining molecular conformation from distance or density data
Solving jigsaw puzzles using image features
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic classification of archaeological pottery sherds
Journal on Computing and Cultural Heritage (JOCCH)
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This paper proposes a new technique for solving jigsaw puzzles. The novelty of the proposed technique is that it provides an automatic jigsaw puzzle solution without any initial restriction about the shape of pieces, the number of neighbor pieces, etc. The proposed technique uses both curve- and color-matching similarity features. A recurrent procedure is applied, which compares and merges puzzle pieces in pairs, until the original puzzle image is reformed. Geometrical and color features are extracted on the characteristic points (CPs) of the puzzle pieces. CPs, which can be considered as high curvature points, are detected by a rotationally invariant corner detection algorithm. The features which are associated with color are provided by applying a color reduction technique using the Kohonen self-organized feature map. Finally, a postprocessing stage checks and corrects the relative position between puzzle pieces to improve the quality of the resulting image. Experimental results prove the efficiency of the proposed technique, which can be further extended to deal with even more complex jigsaw puzzle problems.