Graph-Based Clustering for Apictorial Jigsaw Puzzles of Hand Shredded Content-less Pages
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- 关键词：Content ; less page reassembly ; Partial contour matching ; Shape features ; Agglomerative clustering ; Global reassembly
- 刊名：Lecture Notes in Computer Science
- 出版年：2017
- 出版时间：2017
- 年：2017
- 卷：10127
- 期：1
- 页码：135-147
- 丛书名：Intelligent Human Computer Interaction
- ISBN：978-3-319-52503-7
- 卷排序：10127
文摘
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 content-less pages from arbitrarily torn fragments.