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Computational aesthetics and applications

Computational aesthetics, which bridges science and art, is emerging as a new interdisciplinary field. This paper concentrates on two main aspects of computational aesthetics: aesthetic measurement and quantification, generative art, and then proposes a design generation framework. On aesthetic meas...

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Detalles Bibliográficos
Autores principales: Bo, Yihang, Yu, Jinhui, Zhang, Kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099549/
https://www.ncbi.nlm.nih.gov/pubmed/32240390
http://dx.doi.org/10.1186/s42492-018-0006-1
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author Bo, Yihang
Yu, Jinhui
Zhang, Kang
author_facet Bo, Yihang
Yu, Jinhui
Zhang, Kang
author_sort Bo, Yihang
collection PubMed
description Computational aesthetics, which bridges science and art, is emerging as a new interdisciplinary field. This paper concentrates on two main aspects of computational aesthetics: aesthetic measurement and quantification, generative art, and then proposes a design generation framework. On aesthetic measurement and quantification, we review different types of features used in measurement, the currently used evaluation methods, and their applications. On generative art, we focus on both fractal art and abstract paintings modeled on well-known artists’ styles. In general, computational aesthetics exploits computational methods for aesthetic expressions. In other words, it enables computer to appraise beauty and ugliness and also automatically generate aesthetic images. Computational aesthetics has been widely applied to many areas, such as photography, fine art, Chinese hand-writing, web design, graphic design, and industrial design. We finally propose a design generation methodology, utilizing techniques from both aesthetic measurements and generative art.
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spelling pubmed-70995492020-03-31 Computational aesthetics and applications Bo, Yihang Yu, Jinhui Zhang, Kang Vis Comput Ind Biomed Art Review Computational aesthetics, which bridges science and art, is emerging as a new interdisciplinary field. This paper concentrates on two main aspects of computational aesthetics: aesthetic measurement and quantification, generative art, and then proposes a design generation framework. On aesthetic measurement and quantification, we review different types of features used in measurement, the currently used evaluation methods, and their applications. On generative art, we focus on both fractal art and abstract paintings modeled on well-known artists’ styles. In general, computational aesthetics exploits computational methods for aesthetic expressions. In other words, it enables computer to appraise beauty and ugliness and also automatically generate aesthetic images. Computational aesthetics has been widely applied to many areas, such as photography, fine art, Chinese hand-writing, web design, graphic design, and industrial design. We finally propose a design generation methodology, utilizing techniques from both aesthetic measurements and generative art. Springer Singapore 2018-09-05 /pmc/articles/PMC7099549/ /pubmed/32240390 http://dx.doi.org/10.1186/s42492-018-0006-1 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Review
Bo, Yihang
Yu, Jinhui
Zhang, Kang
Computational aesthetics and applications
title Computational aesthetics and applications
title_full Computational aesthetics and applications
title_fullStr Computational aesthetics and applications
title_full_unstemmed Computational aesthetics and applications
title_short Computational aesthetics and applications
title_sort computational aesthetics and applications
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099549/
https://www.ncbi.nlm.nih.gov/pubmed/32240390
http://dx.doi.org/10.1186/s42492-018-0006-1
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