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Graph-based clustering for apictorial jigsaw puzzles of hand shredded content-less pages
Published in Springer Verlag
2017
Volume: 10127 LNCS
   
Pages: 135 - 147
Abstract
Reassembling hand shredded content-less pages is a challenging task, with applications in forensics and fun games. This paper proposes an efficient iterative framework to solve apictorial jigsaw puzzles of hand shredded content-less pages, using only the shape information. The proposed framework consists of four phases. In the first phase, normalized shape features are extracted from fragment contours. Then, for all possible matches between pairs of fragments transformation parameters for alignment of fragments and three goodness scores are estimated. In the third phase, incorrect matches are eliminated based on the score values. The alignments are refined by pruning the set of pairwise matched fragments. Finally, a modified graph-based framework for agglomerative clustering is used to globally reassemble the page(s). Experimental evaluation of our proposed framework on an annotated dataset of shredded documents shows the efficiency in the reconstruction of multiple contentless pages from arbitrarily torn fragments. © Springer International Publishing AG 2017.
About the journal
JournalData powered by TypesetLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherData powered by TypesetSpringer Verlag
ISSN03029743
Open AccessNo
Concepts (11)
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    Cluster analysis
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    Graphic methods
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    AGGLOMERATIVE CLUSTERING
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    Experimental evaluation
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    GRAPH-BASED CLUSTERING
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    ITERATIVE FRAMEWORK
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    PARTIAL CONTOURS
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    REASSEMBLY
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    SHAPE FEATURES
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    TRANSFORMATION PARAMETERS
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    Human computer interaction