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Vision-Based Jigsaw Puzzle Solving with a Robotic Arm

This study proposed two algorithms for reconstructing jigsaw puzzles by using a color compatibility feature. Two realistic application cases were examined: one involved using the original image, while the other did not. We also calculated the transformation matrix to obtain the real positions of eac...

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Detalles Bibliográficos
Autores principales: Ma, Chang-Hsian, Lu, Chien-Liang, Shih, Huang-Chia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422444/
https://www.ncbi.nlm.nih.gov/pubmed/37571693
http://dx.doi.org/10.3390/s23156913
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author Ma, Chang-Hsian
Lu, Chien-Liang
Shih, Huang-Chia
author_facet Ma, Chang-Hsian
Lu, Chien-Liang
Shih, Huang-Chia
author_sort Ma, Chang-Hsian
collection PubMed
description This study proposed two algorithms for reconstructing jigsaw puzzles by using a color compatibility feature. Two realistic application cases were examined: one involved using the original image, while the other did not. We also calculated the transformation matrix to obtain the real positions of each puzzle piece and transmitted the positional information to the robotic arm, which then put each puzzle piece in its correct position. The algorithms were tested on 35-piece and 70-piece puzzles, achieving an average success rate of 87.1%. Compared with the human visual system, the proposed methods demonstrated enhanced accuracy when handling more complex textural images.
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spelling pubmed-104224442023-08-13 Vision-Based Jigsaw Puzzle Solving with a Robotic Arm Ma, Chang-Hsian Lu, Chien-Liang Shih, Huang-Chia Sensors (Basel) Article This study proposed two algorithms for reconstructing jigsaw puzzles by using a color compatibility feature. Two realistic application cases were examined: one involved using the original image, while the other did not. We also calculated the transformation matrix to obtain the real positions of each puzzle piece and transmitted the positional information to the robotic arm, which then put each puzzle piece in its correct position. The algorithms were tested on 35-piece and 70-piece puzzles, achieving an average success rate of 87.1%. Compared with the human visual system, the proposed methods demonstrated enhanced accuracy when handling more complex textural images. MDPI 2023-08-03 /pmc/articles/PMC10422444/ /pubmed/37571693 http://dx.doi.org/10.3390/s23156913 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ma, Chang-Hsian
Lu, Chien-Liang
Shih, Huang-Chia
Vision-Based Jigsaw Puzzle Solving with a Robotic Arm
title Vision-Based Jigsaw Puzzle Solving with a Robotic Arm
title_full Vision-Based Jigsaw Puzzle Solving with a Robotic Arm
title_fullStr Vision-Based Jigsaw Puzzle Solving with a Robotic Arm
title_full_unstemmed Vision-Based Jigsaw Puzzle Solving with a Robotic Arm
title_short Vision-Based Jigsaw Puzzle Solving with a Robotic Arm
title_sort vision-based jigsaw puzzle solving with a robotic arm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422444/
https://www.ncbi.nlm.nih.gov/pubmed/37571693
http://dx.doi.org/10.3390/s23156913
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