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Computational Approach to Seasonal Changes of Living Leaves

This paper proposes a computational approach to seasonal changes of living leaves by combining the geometric deformations and textural color changes. The geometric model of a leaf is generated by triangulating the scanned image of a leaf using an optimized mesh. The triangular mesh of the leaf is de...

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Detalles Bibliográficos
Autores principales: Tang, Ying, Wu, Dong-Yan, Fan, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3596921/
https://www.ncbi.nlm.nih.gov/pubmed/23533545
http://dx.doi.org/10.1155/2013/619385
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author Tang, Ying
Wu, Dong-Yan
Fan, Jing
author_facet Tang, Ying
Wu, Dong-Yan
Fan, Jing
author_sort Tang, Ying
collection PubMed
description This paper proposes a computational approach to seasonal changes of living leaves by combining the geometric deformations and textural color changes. The geometric model of a leaf is generated by triangulating the scanned image of a leaf using an optimized mesh. The triangular mesh of the leaf is deformed by the improved mass-spring model, while the deformation is controlled by setting different mass values for the vertices on the leaf model. In order to adaptively control the deformation of different regions in the leaf, the mass values of vertices are set to be in proportion to the pixels' intensities of the corresponding user-specified grayscale mask map. The geometric deformations as well as the textural color changes of a leaf are used to simulate the seasonal changing process of leaves based on Markov chain model with different environmental parameters including temperature, humidness, and time. Experimental results show that the method successfully simulates the seasonal changes of leaves.
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spelling pubmed-35969212013-03-26 Computational Approach to Seasonal Changes of Living Leaves Tang, Ying Wu, Dong-Yan Fan, Jing Comput Math Methods Med Research Article This paper proposes a computational approach to seasonal changes of living leaves by combining the geometric deformations and textural color changes. The geometric model of a leaf is generated by triangulating the scanned image of a leaf using an optimized mesh. The triangular mesh of the leaf is deformed by the improved mass-spring model, while the deformation is controlled by setting different mass values for the vertices on the leaf model. In order to adaptively control the deformation of different regions in the leaf, the mass values of vertices are set to be in proportion to the pixels' intensities of the corresponding user-specified grayscale mask map. The geometric deformations as well as the textural color changes of a leaf are used to simulate the seasonal changing process of leaves based on Markov chain model with different environmental parameters including temperature, humidness, and time. Experimental results show that the method successfully simulates the seasonal changes of leaves. Hindawi Publishing Corporation 2013 2013-02-25 /pmc/articles/PMC3596921/ /pubmed/23533545 http://dx.doi.org/10.1155/2013/619385 Text en Copyright © 2013 Ying Tang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Tang, Ying
Wu, Dong-Yan
Fan, Jing
Computational Approach to Seasonal Changes of Living Leaves
title Computational Approach to Seasonal Changes of Living Leaves
title_full Computational Approach to Seasonal Changes of Living Leaves
title_fullStr Computational Approach to Seasonal Changes of Living Leaves
title_full_unstemmed Computational Approach to Seasonal Changes of Living Leaves
title_short Computational Approach to Seasonal Changes of Living Leaves
title_sort computational approach to seasonal changes of living leaves
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3596921/
https://www.ncbi.nlm.nih.gov/pubmed/23533545
http://dx.doi.org/10.1155/2013/619385
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