找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Image Processing and Capsule Networks; ICIPCN 2020 Joy Iong-Zong Chen,João Manuel R. S. Tavares,Abdul Conference proceedings 2021 The Edito

[复制链接]
查看: 34429|回复: 61
发表于 2025-3-21 19:24:17 | 显示全部楼层 |阅读模式
书目名称Image Processing and Capsule Networks
副标题ICIPCN 2020
编辑Joy Iong-Zong Chen,João Manuel R. S. Tavares,Abdul
视频video
概述Presents recent research on Image Processing and Capsule Networks.Includes the proceedings of the International Conference on Image Processing and Capsule Networks (ICIPCN2020), held in Bangkok, Thail
丛书名称Advances in Intelligent Systems and Computing
图书封面Titlebook: Image Processing and Capsule Networks; ICIPCN 2020 Joy Iong-Zong Chen,João Manuel R. S. Tavares,Abdul Conference proceedings 2021 The Edito
描述.This book emphasizes the emerging building block of image processing domain, which is known as capsule networks for performing deep image recognition and processing for next-generation imaging science. Recent years have witnessed the continuous development of technologies and methodologies related to image processing, analysis and 3D modeling which have been implemented in the field of computer and image vision. The significant development of these technologies has led to an efficient solution called capsule networks [CapsNet] to solve the intricate challenges in recognizing complex image poses, visual tasks, and object deformation. Moreover, the breakneck growth of computation complexities and computing efficiency has initiated the significant developments of the effective and sophisticated capsule network algorithms and artificial intelligence [AI] tools into existence. .The main contribution of this book is to explain and summarize the significant state-of-the-art research advances in the areas of capsule network [CapsNet] algorithms and architectures with real-time implications in the areas of image detection, remote sensing, biomedical image analysis, computer communications,
出版日期Conference proceedings 2021
关键词Image Processing; Capsule Networks; Artificial Neural Networks; Neuro-fuzzy Control; Genetic Algorithms;
版次1
doihttps://doi.org/10.1007/978-3-030-51859-2
isbn_softcover978-3-030-51858-5
isbn_ebook978-3-030-51859-2Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

书目名称Image Processing and Capsule Networks影响因子(影响力)




书目名称Image Processing and Capsule Networks影响因子(影响力)学科排名




书目名称Image Processing and Capsule Networks网络公开度




书目名称Image Processing and Capsule Networks网络公开度学科排名




书目名称Image Processing and Capsule Networks被引频次




书目名称Image Processing and Capsule Networks被引频次学科排名




书目名称Image Processing and Capsule Networks年度引用




书目名称Image Processing and Capsule Networks年度引用学科排名




书目名称Image Processing and Capsule Networks读者反馈




书目名称Image Processing and Capsule Networks读者反馈学科排名




单选投票, 共有 0 人参与投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 21:18:28 | 显示全部楼层
Development of an Algorithm for Vertebrae Identification Using Speeded up Robost Features (SURF) Te Then with the help of support vector machine (SVM), the template matching is done to identify the vertebrae. Using this vertebrae identification many other parameters such as calculation of Cobb angle, estimation of truncal shift, and type of deformity can be classified.
发表于 2025-3-22 04:03:29 | 显示全部楼层
发表于 2025-3-22 04:55:35 | 显示全部楼层
发表于 2025-3-22 09:22:21 | 显示全部楼层
发表于 2025-3-22 14:09:20 | 显示全部楼层
发表于 2025-3-22 17:38:44 | 显示全部楼层
发表于 2025-3-22 21:26:03 | 显示全部楼层
发表于 2025-3-23 05:25:50 | 显示全部楼层
2194-5357 ng and Capsule Networks (ICIPCN2020), held in Bangkok, Thail.This book emphasizes the emerging building block of image processing domain, which is known as capsule networks for performing deep image recognition and processing for next-generation imaging science. Recent years have witnessed the conti
发表于 2025-3-23 09:32:40 | 显示全部楼层
Efficient GAN-Based Remote Sensing Image Change Detection Under Noise Conditions,ulti-scale change detection is closely related to the multi-scale change detection, so the result is not avoiding the presence of “salt and pepper” noise. The GANs are integrated to pre-process the images, and the de-noising work is enhanced for the high-resolution images. The experiment has proved its effectiveness.
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-15 00:36
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表