Assembly of puzzles using a genetic algorithm

Toyama, Fubito; Fujiki, Y.; Shoji, K.; Miyamichi, Juichi

doi:10.1109/icpr.2002.1047477

Cited by 26 publications

(27 citation statements)

References 4 publications

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“…By the end of the century, shaped-based solvers were already able to reconstruct puzzles of more than 200 pieces [9]. The use of appearance and chromatic information began only three decades after the work of Freeman and Garder (e.g., [11,6,21,24,23,13,15]). …”

Section: Introductionmentioning

confidence: 99%

“…One approach compares the appearance of the abutting boundaries while using a formal distance measure between these two vectors (e.g., [11,6,21,24,13,15]). Alternatively, it was suggested to use the entire part and measure its statistical properties in order to group similar parts together [15] or as a means to define pairwise similarity measures [11].…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, it was suggested to use the entire part and measure its statistical properties in order to group similar parts together [15] or as a means to define pairwise similarity measures [11]. Once the similarity between two parts is estimated, different classes of algorithms are employed to solve the puzzle, and in particular, previous methods have used greedy algorithms [7,15], dynamic programming [1], genetic algorithms [21] and approximation algorithms on graphical models [5].…”

Section: Introductionmentioning

confidence: 99%

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A fully automated greedy square jigsaw puzzle solver

2011

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A fully automated greedy square jigsaw puzzle solver

2011

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“…Toyama et al [10] proposed a method for solving the rectangle piece jigsaw puzzle problem using a genetic algorithm. The picture of their puzzle is painted only in black and white and so the pieces of the puzzle are represented as binary images.…”

Section: Previous Workmentioning

confidence: 99%

“…We have decided to experiment with rectangular pictorial puzzle as it is described in [10]. Therefore, we assume that it consists of NxM puzzle pieces that do not rotate and the assembly of the puzzle is performed by using the pixel values on the border lines of the individual pieces.…”

Section: Problem Definition and Representationmentioning

confidence: 99%

Sequence Matching Genetic Algorithm for Square Jigsaw Puzzles

Hynek

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Part 7: Genetic AlgorithmsInternational audienceOur paper presents a new method for solving the rectangle piece jigsaw puzzle problem. The puzzle image is RGB full color and because of uniform shape of the individual pieces the process of puzzle assembly is based on information of the pixel values along the border line of the piece only. We have utilized a genetic algorithm that searches for the optimal piece arrangement using dissimilarity between adjacent pieces as the measure of progress. Unlike the previous attempts to utilize genetic algorithms to solve the problem, we have proposed a new heuristic asexual operator that aims at identification of points of fraction within partially assembled picture, extraction of supposed sequence of correctly joint pieces, and its insertion into a new position in such a way that, if possible, the segment is enlarged. Our approach has been successfully tested and the algorithm is capable of solving puzzles consisting of several hundred pieces

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Communication by identity discrimination in bio‐inspired multi‐agent systems

González-Pardo

Varona

Camacho

et al. 2011

Concurrency and Computation

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SUMMARYNetwork communications have been widely studied in the last decades in different research fields: artificial intelligence, computer science, biology, medicine and psychology among others. Some important efforts have been carried out to analyse communication features such as overhead, connectivity or communication protocols in these areas from their own perspectives. When this problem is restricted to intelligent agents or multi-agent systems, networks are built by a set of interconnected agents that can be software or hardware. In multi-agent systems, communication optimization is used to improve the overall performance of the system by reducing the information sharing (i.e. number of messages or message size) between the agents. This paper analyses a scale-free network topology of agents to solve a multi-sorting problem. The agents use their local information as well as a bio-inspired identity discrimination process to select only those messages that are relevant for each agent to solve jigsaw puzzles. We provide a comprehensive study on the influence of some essential parameters (memory information size and reconnection probability) in an agent network, and how they can be set to obtain a better performance in the system. The experiments show that this strategy contributes to reduce the number of iterations needed to solve the problem.

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Assembly of puzzles using a genetic algorithm

Cited by 26 publications

References 4 publications

A fully automated greedy square jigsaw puzzle solver

A fully automated greedy square jigsaw puzzle solver

Sequence Matching Genetic Algorithm for Square Jigsaw Puzzles

Communication by identity discrimination in bio‐inspired multi‐agent systems

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