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Survey of Procedural Methods for Two-Dimensional Texture Generation
Textures are the most important element for simulating real-world scenes and providing realistic and immersive sensations in many applications. Procedural textures can simulate a broad variety of surface textures, which is helpful for the design and development of new sensors. Procedural texture gen...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070409/ https://www.ncbi.nlm.nih.gov/pubmed/32093019 http://dx.doi.org/10.3390/s20041135 |
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author | Dong, Junyu Liu, Jun Yao, Kang Chantler, Mike Qi, Lin Yu, Hui Jian, Muwei |
author_facet | Dong, Junyu Liu, Jun Yao, Kang Chantler, Mike Qi, Lin Yu, Hui Jian, Muwei |
author_sort | Dong, Junyu |
collection | PubMed |
description | Textures are the most important element for simulating real-world scenes and providing realistic and immersive sensations in many applications. Procedural textures can simulate a broad variety of surface textures, which is helpful for the design and development of new sensors. Procedural texture generation is the process of creating textures using mathematical models. The input to these models can be a set of parameters, random values generated by noise functions, or existing texture images, which may be further processed or combined to generate new textures. Many methods for procedural texture generation have been proposed, but there has been no comprehensive survey or comparison of them yet. In this paper, we present a review of different procedural texture generation methods, according to the characteristics of the generated textures. We divide the different generation methods into two categories: structured texture and unstructured texture generation methods. Example textures are generated using these methods with varying parameter values. Furthermore, we survey post-processing methods based on the filtering and combination of different generation models. We also present a taxonomy of different models, according to the mathematical functions and texture samples they can produce. Finally, a psychophysical experiment is designed to identify the perceptual features of the example textures. Finally, an analysis of the results illustrates the strengths and weaknesses of these methods. |
format | Online Article Text |
id | pubmed-7070409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70704092020-03-19 Survey of Procedural Methods for Two-Dimensional Texture Generation Dong, Junyu Liu, Jun Yao, Kang Chantler, Mike Qi, Lin Yu, Hui Jian, Muwei Sensors (Basel) Article Textures are the most important element for simulating real-world scenes and providing realistic and immersive sensations in many applications. Procedural textures can simulate a broad variety of surface textures, which is helpful for the design and development of new sensors. Procedural texture generation is the process of creating textures using mathematical models. The input to these models can be a set of parameters, random values generated by noise functions, or existing texture images, which may be further processed or combined to generate new textures. Many methods for procedural texture generation have been proposed, but there has been no comprehensive survey or comparison of them yet. In this paper, we present a review of different procedural texture generation methods, according to the characteristics of the generated textures. We divide the different generation methods into two categories: structured texture and unstructured texture generation methods. Example textures are generated using these methods with varying parameter values. Furthermore, we survey post-processing methods based on the filtering and combination of different generation models. We also present a taxonomy of different models, according to the mathematical functions and texture samples they can produce. Finally, a psychophysical experiment is designed to identify the perceptual features of the example textures. Finally, an analysis of the results illustrates the strengths and weaknesses of these methods. MDPI 2020-02-19 /pmc/articles/PMC7070409/ /pubmed/32093019 http://dx.doi.org/10.3390/s20041135 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dong, Junyu Liu, Jun Yao, Kang Chantler, Mike Qi, Lin Yu, Hui Jian, Muwei Survey of Procedural Methods for Two-Dimensional Texture Generation |
title | Survey of Procedural Methods for Two-Dimensional Texture Generation |
title_full | Survey of Procedural Methods for Two-Dimensional Texture Generation |
title_fullStr | Survey of Procedural Methods for Two-Dimensional Texture Generation |
title_full_unstemmed | Survey of Procedural Methods for Two-Dimensional Texture Generation |
title_short | Survey of Procedural Methods for Two-Dimensional Texture Generation |
title_sort | survey of procedural methods for two-dimensional texture generation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070409/ https://www.ncbi.nlm.nih.gov/pubmed/32093019 http://dx.doi.org/10.3390/s20041135 |
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