纹理合成结果的神经评价

K. Rouis, M. Sayadi, F. Fnaiech
{"title":"纹理合成结果的神经评价","authors":"K. Rouis, M. Sayadi, F. Fnaiech","doi":"10.1109/ICEESA.2013.6578466","DOIUrl":null,"url":null,"abstract":"This paper introduces a new idea of using the Self-Organizing Map to compare the similarity between the synthesized textures and the original sample in accordance with human visual system. The approaches of texture synthesis do not provide convincing results for all types of textures, since it is difficult to produce a general definition of this term. So that, we propose to evaluate the quality of results provided by different approaches, based on features of comparison to capture information present in texture image.","PeriodicalId":212631,"journal":{"name":"2013 International Conference on Electrical Engineering and Software Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural evaluation of texture synthesis results\",\"authors\":\"K. Rouis, M. Sayadi, F. Fnaiech\",\"doi\":\"10.1109/ICEESA.2013.6578466\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a new idea of using the Self-Organizing Map to compare the similarity between the synthesized textures and the original sample in accordance with human visual system. The approaches of texture synthesis do not provide convincing results for all types of textures, since it is difficult to produce a general definition of this term. So that, we propose to evaluate the quality of results provided by different approaches, based on features of comparison to capture information present in texture image.\",\"PeriodicalId\":212631,\"journal\":{\"name\":\"2013 International Conference on Electrical Engineering and Software Applications\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Electrical Engineering and Software Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEESA.2013.6578466\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Electrical Engineering and Software Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEESA.2013.6578466","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍了一种利用自组织映射(Self-Organizing Map)来比较合成纹理与原始纹理之间的相似性的新思路。纹理合成的方法不能为所有类型的纹理提供令人信服的结果,因为很难对这一术语给出一个通用的定义。因此,我们建议基于比较的特征来评估不同方法提供的结果的质量,以捕获纹理图像中存在的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural evaluation of texture synthesis results
This paper introduces a new idea of using the Self-Organizing Map to compare the similarity between the synthesized textures and the original sample in accordance with human visual system. The approaches of texture synthesis do not provide convincing results for all types of textures, since it is difficult to produce a general definition of this term. So that, we propose to evaluate the quality of results provided by different approaches, based on features of comparison to capture information present in texture image.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信