Liu, H.R.[Hai-Rong],
Cao, S.J.[Sheng-Jiao],
Yan, S.C.[Shui-Cheng],
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MultMed(13), No. 5, 2011, pp. 1154-1162.
IEEE DOI
1110
BibRef
Geller, T.[Tom],
DARPA Shredder Challenge Solved,
CACM(55), No. 8, August 2012, pp. 16-17.
DOI Link
1208
In the end some human help, but the crowdsourced version failed (to win, it did
complete the task). A lot of automation used.
BibRef
Richter, F.,
Ries, C.X.,
Cebron, N.,
Lienhart, R.,
Learning to Reassemble Shredded Documents,
MultMed(15), No. 3, 2013, pp. 582-593.
IEEE DOI
1303
BibRef
Andaló, F.A.[Fernanda A.],
Taubin, G.[Gabriel],
Goldenstein, S.[Siome],
PSQP: Puzzle Solving by Quadratic Programming,
PAMI(39), No. 2, February 2017, pp. 385-396.
IEEE DOI
1702
maximization of a constrained quadratic function.
E.g. Shredded documents.
BibRef
Le, C.,
Li, X.,
JigsawNet: Shredded Image Reassembly Using Convolutional Neural
Network and Loop-Based Composition,
IP(28), No. 8, August 2019, pp. 4000-4015.
IEEE DOI
1907
graph theory, greedy algorithms, image classification,
image matching, learning (artificial intelligence), neural nets,
loop closure constraints
BibRef
Liang, Y.,
Li, X.,
Reassembling Shredded Document Stripes Using Word-Path Metric and
Greedy Composition Optimal Matching Solver,
MultMed(22), No. 5, May 2020, pp. 1168-1181.
IEEE DOI
2005
Measurement, Optical character recognition software,
Optimal matching, Image reconstruction,
global reconstruction from local alignments
BibRef
Paixão, T.M.[Thiago M.],
Berriel, R.F.[Rodrigo F.],
Boeres, M.C.S.[Maria C.S.],
Koerich, A.L.[Alessandro L.],
Badue, C.[Claudine],
de Souza, A.F.[Alberto F.],
Oliveira-Santos, T.[Thiago],
Self-supervised deep reconstruction of mixed strip-shredded text
documents,
PR(107), 2020, pp. 107535.
Elsevier DOI
2008
BibRef
And:
Fast(er) Reconstruction of Shredded Text Documents via
Self-Supervised Deep Asymmetric Metric Learning,
CVPR20(14331-14339)
IEEE DOI
2008
Measurement, Mathematical model, Task analysis, Forensics, Manuals,
Machine learning, Shape.
Deep learning, Self-supervised learning,
Fully convolutional neural networks, Document reconstruction,
Optimization search
BibRef
de Lima-Hernandez, R.[Roberto],
Vergauwen, M.[Maarten],
A Generative and Entropy-Based Registration Approach for the
Reassembly of Ancient Inscriptions,
RS(14), No. 1, 2022, pp. xx-yy.
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2201
BibRef
Paixão, T.M.[Thiago M.],
Berriel, R.F.[Rodrigo F.],
Boeres, M.C.S.[Maria C.S.],
Koerich, A.L.[Alessandro L.],
Badue, C.[Claudine],
de Souza, A.F.[Alberto F.],
Oliveira-Santos, T.[Thiago],
A human-in-the-loop recommendation-based framework for reconstruction
of mechanically shredded documents,
PRL(164), 2022, pp. 1-8.
Elsevier DOI
2212
Document reconstruction, Forensics, Jigsaw puzzle solving,
Deep learning, Active learning
BibRef
Shang, S.Z.[Shi-Ze],
Sencar, H.T.[Husrev T.],
Memon, N.[Nasir],
Kong, X.W.[Xiang-Wei],
A semi-automatic deshredding method based on curve matching,
ICIP14(5537-5541)
IEEE DOI
1502
Color
BibRef
Saboia, P.[Priscila],
Goldenstein, S.K.[Siome K.],
Assessing Cross-Cut Shredded Document Assembly,
CIARP14(272-279).
Springer DOI
1411
BibRef
Ranca, R.[Razvan],
Murray, I.[Iain],
A Composable Strategy for Shredded Document Reconstruction,
CAIP13(II:324-331).
Springer DOI
1311
BibRef
Deever, A.[Aaron],
Gallagher, A.[Andrew],
Semi-automatic assembly of real cross-cut shredded documents,
ICIP12(233-236).
IEEE DOI
1302
BibRef
Lin, H.Y.[Huei-Yung],
Fan-Chiang, W.C.[Wen-Cheng],
Image-Based Techniques for Shredded Document Reconstruction,
PSIVT09(155-166).
Springer DOI
0901
BibRef
Ukovich, A.,
Ramponi, G.,
Features for the Reconstruction of Shredded Notebook Paper,
ICIP05(III: 93-96).
IEEE DOI
0512
BibRef
Biswas, A.,
Bhowmick, P.[Partha],
Bhattacharya, B.B.[Bhargab B.],
Reconstruction of Torn Documents Using Contour Maps,
ICIP05(III: 517-520).
IEEE DOI
0512
BibRef
Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Matching, Affine Transformations .