27,191 research outputs found
Consistent Correspondences for Shape and Image Problems
Establish consistent correspondences between different objects is a classic problem in computer science/vision. It helps to match highly similar objects in both 3D and 2D domain. Inthe 3D domain, finding consistent correspondences has been studying for more than 20 yearsand it is still a hot topic. In 2D domain, consistent correspondences can also help in puzzlesolving. However, only a few works are focused on this approach. In this thesis, we focuson finding consistent correspondences and extend to develop robust matching techniques inboth 3D shape segments and 2D puzzle solving. In the 3D domain, segment-wise matching isan important research problem that supports higher-level understanding of shapes in geometryprocessing. Many existing segment-wise matching techniques assume perfect input segmentation and would suffer from imperfect or over-segmented input. To handle this shortcoming,we propose multi-layer graphs (MLGs) to represent possible arrangements of partially mergedsegments of input shapes. We then adapt the diffusion pruning technique on the MLGs to findconsistent segment-wise matching. To obtain high-quality matching, we develop our own voting step which is able to remove inconsistent results, for finding hierarchically consistent correspondences as final output. We evaluate our technique with both quantitative and qualitativeexperiments on both man-made and deformable shapes. Experimental results demonstrate theeffectiveness of our technique when compared to two state-of-art methods. In the 2D domain,solving jigsaw puzzles is also a classic problem in computer vision with various applications.Over the past decades, many useful approaches have been introduced. Most existing worksuse edge-wise similarity measures for assembling puzzles with square pieces of the same size, and recent work innovates to use the loop constraint to improve efficiency and accuracy. Weobserve that most existing techniques cannot be easily extended to puzzles with rectangularpieces of arbitrary sizes, and no existing loop constraints can be used to model such challenging scenarios. We propose new matching approaches based on sub-edges/corners, modelledusing the MatchLift or diffusion framework to solve square puzzles with cycle consistency.We demonstrate the robustness of our approaches by comparing our methods with state-of-artmethods. We also show how puzzles with rectangular pieces of arbitrary sizes, or puzzles withtriangular and square pieces can be solved by our techniques
Optimization for automated assembly of puzzles
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 formulated as an optimization problem where the optimum assembly of the pieces is achieved by maximizing the total affinity measure. An fft based image registration technique is used to speed up the alignment of the pieces. Experimental results are presented on real and artificial data sets
Characterising Players of a Cube Puzzle Game with a Two-level Bag of Words
Ponencia presentada en UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, Utrecht (Netherlands), June 21 - 25, 2021This work explores an unsupervised approach for modelling players
of a 2D cube puzzle game with the ultimate goal of customising the
game for particular players based solely on their interaction data.
To that end, user interactions when solving puzzles are coded as
images. Then, a feature embedding is learned for each puzzle with
a convolutional network trained to regress the players’ comple tion effort in terms of time and number of clicks. Next, the known
bag-of-words technique is used at two levels. First, sets of puzzles
are represented using the puzzle feature embeddings as the input
space. Second, the resulting first-level histograms are used as input
space for characterising players. As a result, new players can be
characterised in terms of the resulting second-level histograms.
Preliminary results indicate that the approach is effective for char acterising players in terms of performance. It is also tentatively
observed that other personal perceptions and preferences, beyond
performance, are somehow implicitly captured from behavioural
data
A Global Approach for Solving Edge-Matching Puzzles
We consider apictorial edge-matching puzzles, in which the goal is to arrange
a collection of puzzle pieces with colored edges so that the colors match along
the edges of adjacent pieces. We devise an algebraic representation for this
problem and provide conditions under which it exactly characterizes a puzzle.
Using the new representation, we recast the combinatorial, discrete problem of
solving puzzles as a global, polynomial system of equations with continuous
variables. We further propose new algorithms for generating approximate
solutions to the continuous problem by solving a sequence of convex
relaxations
Solving Jigsaw Puzzles By the Graph Connection Laplacian
We propose a novel mathematical framework to address the problem of
automatically solving large jigsaw puzzles. This problem assumes a large image,
which is cut into equal square pieces that are arbitrarily rotated and
shuffled, and asks to recover the original image given the transformed pieces.
The main contribution of this work is a method for recovering the rotations of
the pieces when both shuffles and rotations are unknown. A major challenge of
this procedure is estimating the graph connection Laplacian without the
knowledge of shuffles. We guarantee some robustness of the latter estimate to
measurement errors. A careful combination of our proposed method for estimating
rotations with any existing method for estimating shuffles results in a
practical solution for the jigsaw puzzle problem. Numerical experiments
demonstrate the competitive accuracy of this solution, its robustness to
corruption and its computational advantage for large puzzles
Foundations, Properties, and Security Applications of Puzzles: A Survey
Cryptographic algorithms have been used not only to create robust ciphertexts
but also to generate cryptograms that, contrary to the classic goal of
cryptography, are meant to be broken. These cryptograms, generally called
puzzles, require the use of a certain amount of resources to be solved, hence
introducing a cost that is often regarded as a time delay---though it could
involve other metrics as well, such as bandwidth. These powerful features have
made puzzles the core of many security protocols, acquiring increasing
importance in the IT security landscape. The concept of a puzzle has
subsequently been extended to other types of schemes that do not use
cryptographic functions, such as CAPTCHAs, which are used to discriminate
humans from machines. Overall, puzzles have experienced a renewed interest with
the advent of Bitcoin, which uses a CPU-intensive puzzle as proof of work. In
this paper, we provide a comprehensive study of the most important puzzle
construction schemes available in the literature, categorizing them according
to several attributes, such as resource type, verification type, and
applications. We have redefined the term puzzle by collecting and integrating
the scattered notions used in different works, to cover all the existing
applications. Moreover, we provide an overview of the possible applications,
identifying key requirements and different design approaches. Finally, we
highlight the features and limitations of each approach, providing a useful
guide for the future development of new puzzle schemes.Comment: This article has been accepted for publication in ACM Computing
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