Graph-Based Clustering for Apictorial Jigsaw Puzzles of Hand Shredded Content-less Pages
12 Dec 2016-pp 135-147
TL;DR: An efficient iterative framework to solve apictorial jigsaw puzzles of hand shredded content-less pages, using only the shape information is proposed, which shows the efficiency in the reconstruction of multiple content- less pages from arbitrarily torn fragments.
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 content-less pages from arbitrarily torn fragments.
Citations
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TL;DR: This paper presents a fully-automatic and general algorithm that addresses puzzle solving in archaeology, and shows that the state-of-the-art approach manages to correctly reassemble dozens of broken artifacts and frescoes.
Abstract: This paper focuses on the re-assembly of an archaeological artifact, given images of its fragments. This problem can be considered as a special challenging case of puzzle solving. The restricted case of re-assembly of a natural image from square pieces has been investigated extensively and was shown to be a difficult problem in its own right. Likewise, the case of matching “clean” 2D polygons/splines based solely on their geometric properties has been studied. But what if these ideal conditions do not hold? This is the problem addressed in the paper. Three unique characteristics of archaeological fragments make puzzle solving extremely difficult: (1) The fragments are of general shape; (2) They are abraded, especially at the boundaries (where the strongest cues for matching should exist); and (3) The domain of valid transformations between the pieces is continuous. The key contribution of this paper is a fully-automatic and general algorithm that addresses puzzle solving in this intriguing domain. We show that our approach manages to correctly reassemble dozens of broken artifacts and frescoes.
9 citations
22 Jul 2018
TL;DR: A reassembly framework for a three-dimensional shell is proposed as a logical extension of the twodimensional framework that can handle fragments even with curved edges which can be reasonably approximated by a set of edges.
Abstract: A major bottleneck activity in the process of restoration of Heritage Structures is the reassembly of its fragments. Computer-aided reassembly could assist in finding the relation between them thereby reducing time, manpower and potential degradation to fragile fragments. Using geometric compatibility between the adjacent fragments as the central idea, a reassembly framework for a three-dimensional shell is proposed as a logical extension of the twodimensional framework. Edges are extracted as polygons and relevant features are computed at each of its vertices. Sequences of the match for two fragments in the feature space are found using a modified version of Smith-Waterman Algorithm. Each match is assessed using a connectivity score. The final choice of best match is left to the user by displaying the resultant assembled fragments of prospective candidates along with the score. After pairwise matching, the global reassembly is done through a clustering-based method. This framework can handle fragments even with curved edges which can be reasonably approximated by a set of edges. We verify the methodology using a simulated dataset for both 2D pieces and a shattered 3D
5 citations
Cites background or methods from "Graph-Based Clustering for Apictori..."
...A similar problem was attempted in [12] and [13] where the fragments were converted to invariant features and compared in the feature space....
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...With this reduced number of points in the contour which better capture the shape variation, we extract the following features based on [12] at every vertex: I....
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...not involve the user at all and perform a completely automatic reassembly [12] [14]....
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...As proposed in [12], we use an agglomerative clustering algorithm....
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...The sequences greater than a minimum connectivity value C(i,j) as defined in [12] is stored in G....
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3 citations
TL;DR: This paper proposes a method of completely clustering Chinese homologous pieces in which the distribution features of the characters in the pieces and the document layout are used to correlate adjacent pieces and cluster them in different areas of a document.
Abstract: When recovering a shredded document that has numerous mixed pieces, the difficulty of the recovery process can be reduced by clustering, which is a method of grouping pieces that originally belonged to the same page. Restoring homologous shredded documents (pieces from different pages of the same file) is a frequent problem, and because these pieces have nearly indistinguishable visual characteristics, grouping them is extremely difficult. Clustering research has important practical significance for document recovery because homologous pieces are ubiquitous. Because of the wide usage of Chinese and the huge demand for Chinese shredded document recovery, our research focuses on Chinese homologous pieces. In this paper, we propose a method of completely clustering Chinese homologous pieces in which the distribution features of the characters in the pieces and the document layout are used to correlate adjacent pieces and cluster them in different areas of a document. The experimental results show that the proposed method has a good clustering effect on real pieces. For the dataset containing 10 page documents (a total of 462 pieces), its average accuracy is 97.19%.
2 citations
Cites methods from "Graph-Based Clustering for Apictori..."
...[17] applied the shape information of pieces as the matching feature, clustered the different pages shredded by hand and reassembled the pieces....
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24 Nov 2022
TL;DR: PuzzleFusion as discussed by the authors proposes an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks, which takes a set of room layouts as polygonal curves in the top-down view and aligns the room layout pieces by estimating their 2D translations and rotations.
Abstract: This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks. In the latter task, for instance, the proposed system ``PuzzleFusion'' takes a set of room layouts as polygonal curves in the top-down view and aligns the room layout pieces by estimating their 2D translations and rotations, akin to solving the jigsaw puzzle of room layouts. A surprising discovery of the paper is that the simple use of a Diffusion Model effectively solves these challenging spatial puzzle tasks as a conditional generation process. To enable learning of an end-to-end neural system, the paper introduces new datasets with ground-truth arrangements: 1) 2D Voronoi jigsaw dataset, a synthetic one where pieces are generated by Voronoi diagram of 2D pointset; and 2) MagicPlan dataset, a real one offered by MagicPlan from its production pipeline, where pieces are room layouts constructed by augmented reality App by real-estate consumers. The qualitative and quantitative evaluations demonstrate that our approach outperforms the competing methods by significant margins in all the tasks. We will publicly share all our code and data.
References
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TL;DR: A shape representation suitable for efficient matching (fitting together) of parts of boundaries of two-dimensional objects is described, which uses polar coordinate systems centered around curvature maxima and minima.
Abstract: A shape representation suitable for efficient matching (fitting together) of parts of boundaries of two-dimensional objects is described. This representation, which uses polar coordinate systems centered around curvature maxima and minima, is applied to the problem of fitting together curved jigsaw puzzle pieces. A program implementing these techniques is described.
73 citations
TL;DR: A weighted graph whose vertices represent shredded pieces and edges represent candidate matchings is constructed, and divided into separate subgraphs, with each subgraph corresponding to a desired photo.
Abstract: In this paper, we investigate the problem of automated assembly of shredded pieces from multiple photos, which has a board usage in many multimedia applications. Both shape and appearance information along the boundaries are utilized and extracted for each pieces, and then the candidate matchings between pieces are established based on these features. A weighted graph, called matching graph, whose vertices represent shredded pieces and edges represent candidate matchings is then constructed, and divided into separate subgraphs, with each subgraph corresponding to a desired photo. The assembly results are finally obtained by searching for a valid spanning tree for each subgraph. This proposed method can deal with cases in which materials are lost and/or pieces belonging to multiple photos coexist. And the experimental results well demonstrate the effectiveness and efficiency of our proposed method.
55 citations
TL;DR: An integrated method for automatic color based 2-D image fragment reassembly is presented and it is shown that the most robust algorithms having the best performance are investigated and their results are fed to the next step.
Abstract: The problem of reassembling image fragments arises in many scientific fields, such as forensics and archaeology. In the field of archaeology, the pictorial excavation findings are almost always in the form of painting fragments. The manual execution of this task is very difficult, as it requires great amount of time, skill and effort. Thus, the automation of such a work is very important and can lead to faster, more efficient, painting reassembly and to a significant reduction in the human effort involved. In this paper, an integrated method for automatic color based 2-D image fragment reassembly is presented. The proposed 2-D reassembly technique is divided into four steps. Initially, the image fragments which are probably spatially adjacent, are identified utilizing techniques employed in content based image retrieval systems. The second operation is to identify the matching contour segments for every retained couple of image fragments, via a dynamic programming technique. The next step is to identify the optimal transformation in order to align the matching contour segments. Many registration techniques have been evaluated to this end. Finally, the overall image is reassembled from its properly aligned fragments. This is achieved via a novel algorithm, which exploits the alignment angles found during the previous step. In each stage, the most robust algorithms having the best performance are investigated and their results are fed to the next step. We have experimented with the proposed method using digitally scanned images of actual torn pieces of paper image prints and we produced very satisfactory reassembly results.
51 citations
TL;DR: A method for automatically solving apictorial jigsaw puzzles that is based on an extension of the method of differential invariant signatures, designed to solve challenging puzzles, without having to impose any restrictive assumptions on the shape of the puzzle, the shapes of the individual pieces, or their intrinsic arrangement.
Abstract: We present a method for automatically solving apictorial jigsaw puzzles that is based on an extension of the method of differential invariant signatures. Our algorithms are designed to solve challenging puzzles, without having to impose any restrictive assumptions on the shape of the puzzle, the shapes of the individual pieces, or their intrinsic arrangement. As a demonstration, the method was successfully used to solve two commercially available puzzles. Finally we perform some preliminary investigations into scalability of the algorithm for even larger puzzles.
46 citations