Solving jigsaw puzzles by computer
Annals of Operations Research
A Statistical, Nonparametric Methodology for Document Degradation Model Validation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms
Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms
A Multiscale Method for the Reassembly of Two-Dimensional Fragmented Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracing the Source of a Shredded Document
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
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
Image Restoration of Arbitrarily Warped Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric and Photometric Restoration of Distorted Documents
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Restoring Warped Document Images through 3D Shape Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Consistent Reconstruction of Ripped-Up Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Rectification of Camera-Captured Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perspective rectification of document images using fuzzy set and morphological operations
Image and Vision Computing
| Hi-index | 12.05 |
This paper presents an image-based technique for shredded document reconstruction. Currently, most research on document recovery focuses on image feature exaction and analysis. In this work, we have presented a complete procedure to recover a shredded document. The problem is different from solving jigsaw puzzles since curved boundaries and color information are not available. In our two-stage reconstruction approach, image-based techniques are first used to identify the shred images with high spatial proximity and evaluate the similarity between any pair of shreds. A graph-based algorithm is then used to derive the best shred sorting result for document reconstruction. Experiments are presented for both the synthetic and real datasets.