Solving jigsaw puzzles by computer
Annals of Operations Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence - Special volume on computer vision
Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision
Reconstruction of Three-Dimensional Objects through Matching of Their Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Structural Matching in Computer Vision Using Probabilistic Relaxation
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Similarity and Affine Invariant Distances Between 2D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Segmentation of Curves into Various Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiscale Method for the Reassembly of Two-Dimensional Fragmented Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Curvature and Tangent Direction by Median Filtered Differencing
ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
A shape and image merging technique to solve jigsaw puzzles
Pattern Recognition Letters
Matching of 3-D curves using semi-differential invariants
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Similarity-Based Approach to Earthenware Reconstruction
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A global approach to automatic solution of jigsaw puzzles
Computational Geometry: Theory and Applications - Special issue on the 18th annual symposium on computational geometry—SoCG2002
Document Reconstruction Based on Feature Matching
SIBGRAPI '05 Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing
Contour-shape based reconstruction of fragmented, 1600 BC wallpaintings
IEEE Transactions on Signal Processing
Image-Based Techniques for Shredded Document Reconstruction
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
SPIRE '09 Proceedings of the 16th International Symposium on String Processing and Information Retrieval
Automatic color based reassembly of fragmented images and paintings
IEEE Transactions on Image Processing
Edge envelope based reconstruction of torn document
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Reconstruction of shredded document based on image feature matching
Expert Systems with Applications: An International Journal
Journal of Discrete Algorithms
Efficient solution to the 3D problem of automatic wall paintings reassembly
Computers & Mathematics with Applications
Global consistency in the automatic assembly of fragmented artefacts
VAST'11 Proceedings of the 12th International conference on Virtual Reality, Archaeology and Cultural Heritage
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One of the most crucial steps for automatically reconstructing ripped-up documents is to find a globally consistent solution from the ambiguous candidate matches. However, little work has been done so far to solve this problem in a general computational framework without using application-specific features. In this paper, we propose a global approach for reconstructing ripped-up documents by first finding candidate matches from document fragments using curve matching and then disambiguating these candidates through a relaxation process to reconstruct the original document. The candidate disambiguation problem is formulated in a relaxation scheme, in which the definition of compatibility between neighboring matches is proposed and global consistency is defined as the global criterion. Initially, global match confidences are assigned to each of the candidate matches. After that, the overall local relationships among neighboring matches are evaluated by computing their global consistency. Then these confidences are iteratively updated using the gradient projection method to maximize the criterion. This leads to a globally consistent solution and thus provides a sound document reconstruction. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the reconstruction of ripped-up documents up to fifty pieces is possibly accomplished automatically.