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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05 Jun 2002
TL;DR: The overall strategy follows that of previous algorithms but applies a number of new ideas, such as robust fiducial points, "highest- confidence-first" search, and frequent global reoptimization of partial solutions.
Abstract: We present a new algorithm for automatically solving jigsaw puzzles by shape alone. The algorithm can solve more difficult puzzles than could be solved before, without the use of backtracking or branch-and-bound. The algorithm can handle puzzles in which pieces border more than four neighbors, and puzzles with as many as 200 pieces. Our overall strategy follows that of previous algorithms but applies a number of new ideas, such as robust fiducial points, "highest- confidence-first" search, and frequent global reoptimization of partial solutions.
151 citations
TL;DR: A global approach for reconstructing ripped-up documents by first finding candidate matches from document fragments using curve matching and then disambiguating these candidates through a relaxation process to reconstruct the original document, indicating that the reconstruction of ripped- up documents up to 50 pieces is possibly accomplished automatically.
Abstract: One of the most crucial steps for automatically reconstructing ripped-up documents is to find a globally consistent solution from the ambiguous candidate matches. However, little work has been done so far to solve this problem in a general computational framework without using application-specific features. In this paper, we propose a global approach for reconstructing ripped-up documents by first finding candidate matches from document fragments using curve matching and then disambiguating these candidates through a relaxation process to reconstruct the original document. The candidate disambiguation problem is formulated in a relaxation scheme in which the definition of compatibility between neighboring matches is proposed, and global consistency is defined as the global criterion. Initially, global match confidences are assigned to each of the candidate matches. After that, the overall local relationships among neighboring matches are evaluated by computing their global consistency. Then, these confidences are iteratively updated using the gradient projection method to maximize the criterion. This leads to a globally consistent solution and, thus, provides a sound document reconstruction. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the reconstruction of ripped-up documents up to 50 pieces is possibly accomplished automatically.
134 citations
TL;DR: The proposed method first applies a polygonal approximation in order to reduce the complexity of the boundaries and then extracts relevant features of the polygon to carry out the local reconstruction.
Abstract: We describe a procedure for reconstructing documents that have been shredded by hand, a problem that often arises in forensics The proposed method first applies a polygonal approximation in order to reduce the complexity of the boundaries and then extracts relevant features of the polygon to carry out the local reconstruction In this way, the overall complexity can be dramatically reduced because few features are used to perform the matching The ambiguities resulting from the local reconstruction are resolved and the pieces are merged together as we search for a global solution The preliminary results reported in this paper, which take into account a limited amount of shredded pieces (10-15) demonstrate that feature-matching-based procedure produces interesting results for the problem of document reconstruction
100 citations
20 Aug 2006
TL;DR: A new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints, and the optimization of total affinity gives the best assembly of puzzle.
Abstract: The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The puzzle pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is carried out using an FFT based image registration technique. The optimization of total affinity gives the best assembly of puzzle. Experimental results are presented on real and artificial data sets.
81 citations
21 Oct 2006
TL;DR: A worst-case lower bound and a smoothed upper bound on the number of iterations performed by the iterative closest point (ICP) algorithm are shown and the smoothed complexity of ICP is polynomial, independent of the dimensionality of the data.
Abstract: We show a worst-case lower bound and a smoothed upper bound on the number of iterations performed by the Iterative Closest Point (ICP) algorithm. First proposed by Besl and McKay, the algorithm is widely used in computational geometry where it is known for its simplicity and its observed speed. The theoretical study of ICP was initiated by Ezra, Sharir and Efrat, who bounded its worst-case running time between \Omega(n log n) and O(n^{2}d)^{d}. We substantially tighten this gap by improving the lower bound to \Omega(n/d)^{d+1}. To help reconcile this bound with the algorithm?s observed speed, we also show the smoothed complexity of ICP is polynomial, independent of the dimensionality of the data. Using similar methods, we improve the best known smoothed upper bound for the popular k-means method to n^{O(k)}, once again independent of the dimension.
77 citations