超复杂信号与图像处理:第 1 部分 [特邀编辑寄语]

IF 9.4 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Magazine Pub Date : 2024-03-01 DOI:10.1109/MSP.2024.3378129
Nektarios A. Valous;Eckhard Hitzer;Salvatore Vitabile;Swanhild Bernstein;Carlile Lavor;Derek Abbott;Maria Elena Luna-Elizarrarás;Wilder Lopes
{"title":"超复杂信号与图像处理:第 1 部分 [特邀编辑寄语]","authors":"Nektarios A. Valous;Eckhard Hitzer;Salvatore Vitabile;Swanhild Bernstein;Carlile Lavor;Derek Abbott;Maria Elena Luna-Elizarrarás;Wilder Lopes","doi":"10.1109/MSP.2024.3378129","DOIUrl":null,"url":null,"abstract":"Novel computational signal and image analysis methodologies based on feature-rich mathematical/computational frameworks continue to push the limits of the technological envelope, thus providing optimized and efficient solutions. Hypercomplex signal and image processing is a fascinating field that extends conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. Methodologies that are developed within this field can lead to more effective and powerful ways to analyze signals and images. Processing audio, video, images, and other types of data in the hypercomplex domain allows for more complex and intuitive representations with algebraic properties that can lead to new insights and optimizations. Applications in image processing, signal filtering, and deep learning (just to name a few) have shown that working in the hypercomplex domain can lead to more efficient and robust outcomes. As research in this field progresses and software tools become more widely available, we can expect to see increasingly sophisticated applications in many areas of research, e.g., computer vision, machine learning, and so on.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 2","pages":"11-13"},"PeriodicalIF":9.4000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10558747","citationCount":"0","resultStr":"{\"title\":\"Hypercomplex Signal and Image Processing: Part 1 [From the Guest Editors]\",\"authors\":\"Nektarios A. Valous;Eckhard Hitzer;Salvatore Vitabile;Swanhild Bernstein;Carlile Lavor;Derek Abbott;Maria Elena Luna-Elizarrarás;Wilder Lopes\",\"doi\":\"10.1109/MSP.2024.3378129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Novel computational signal and image analysis methodologies based on feature-rich mathematical/computational frameworks continue to push the limits of the technological envelope, thus providing optimized and efficient solutions. Hypercomplex signal and image processing is a fascinating field that extends conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. Methodologies that are developed within this field can lead to more effective and powerful ways to analyze signals and images. Processing audio, video, images, and other types of data in the hypercomplex domain allows for more complex and intuitive representations with algebraic properties that can lead to new insights and optimizations. Applications in image processing, signal filtering, and deep learning (just to name a few) have shown that working in the hypercomplex domain can lead to more efficient and robust outcomes. As research in this field progresses and software tools become more widely available, we can expect to see increasingly sophisticated applications in many areas of research, e.g., computer vision, machine learning, and so on.\",\"PeriodicalId\":13246,\"journal\":{\"name\":\"IEEE Signal Processing Magazine\",\"volume\":\"41 2\",\"pages\":\"11-13\"},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10558747\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Signal Processing Magazine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10558747/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Magazine","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10558747/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

基于功能丰富的数学/计算框架的新型计算信号和图像分析方法不断突破技术极限,从而提供优化和高效的解决方案。超复数信号和图像处理是一个引人入胜的领域,它通过在代数和几何的统一框架中使用超复数来扩展传统方法。在这一领域中开发的方法可以带来更有效、更强大的信号和图像分析方法。在超复数领域处理音频、视频、图像和其他类型的数据,可以获得更复杂、更直观的表示方法,其代数特性可以带来新的见解和优化。图像处理、信号滤波和深度学习(仅举几例)中的应用表明,在超复杂域中工作可以带来更高效、更稳健的结果。随着该领域研究的不断深入和软件工具的日益普及,我们有望在计算机视觉、机器学习等多个研究领域看到越来越复杂的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Hypercomplex Signal and Image Processing: Part 1 [From the Guest Editors]
Novel computational signal and image analysis methodologies based on feature-rich mathematical/computational frameworks continue to push the limits of the technological envelope, thus providing optimized and efficient solutions. Hypercomplex signal and image processing is a fascinating field that extends conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. Methodologies that are developed within this field can lead to more effective and powerful ways to analyze signals and images. Processing audio, video, images, and other types of data in the hypercomplex domain allows for more complex and intuitive representations with algebraic properties that can lead to new insights and optimizations. Applications in image processing, signal filtering, and deep learning (just to name a few) have shown that working in the hypercomplex domain can lead to more efficient and robust outcomes. As research in this field progresses and software tools become more widely available, we can expect to see increasingly sophisticated applications in many areas of research, e.g., computer vision, machine learning, and so on.
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
IEEE Signal Processing Magazine
IEEE Signal Processing Magazine 工程技术-工程:电子与电气
CiteScore
27.20
自引率
0.70%
发文量
123
审稿时长
6-12 weeks
期刊介绍: EEE Signal Processing Magazine is a publication that focuses on signal processing research and applications. It publishes tutorial-style articles, columns, and forums that cover a wide range of topics related to signal processing. The magazine aims to provide the research, educational, and professional communities with the latest technical developments, issues, and events in the field. It serves as the main communication platform for the society, addressing important matters that concern all members.
期刊最新文献
Front Cover Table of Contents Masthead Special Issue: Artificial Intelligence for Education: A Signal Processing Perspective The Future of Bionic Limbs: The untapped synergy of signal processing, control, and wireless connectivity
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1