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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10422444 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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