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: Mathematical Methods in Image Processing and Inverse Problems; IPIP 2018, Beijing, Xue-Cheng Tai,Suhua Wei,Haiguang Liu Conference proceed

[复制链接]
查看: 26528|回复: 46
发表于 2025-3-21 17:42:24 | 显示全部楼层 |阅读模式
书目名称Mathematical Methods in Image Processing and Inverse Problems
副标题IPIP 2018, Beijing,
编辑Xue-Cheng Tai,Suhua Wei,Haiguang Liu
视频video
概述Includes 11 original research papers by invited speakers at IPIP2018 in honor of Professor Raymond Chan.Deals with efficient algorithms and advanced mathematical modeling in imaging processing, data s
丛书名称Springer Proceedings in Mathematics & Statistics
图书封面Titlebook: Mathematical Methods in Image Processing and Inverse Problems; IPIP 2018, Beijing,  Xue-Cheng Tai,Suhua Wei,Haiguang Liu Conference proceed
描述This book contains eleven original and survey scientific research articles arose from presentationsgiven by invited speakers at International Workshop on Image Processing and Inverse Problems, heldin Beijing Computational Science Research Center, Beijing, China, April 21–24, 2018. The book wasdedicated to Professor Raymond Chan on the occasion of his 60th birthday..The contents of the book cover topics including image reconstruction, image segmentation, imageregistration, inverse problems and so on. Deep learning, PDE, statistical theory based researchmethods and techniques were discussed. The state-of-the-art developments on mathematical analysis,advanced modeling, efficient algorithm and applications were presented. The collected papers in thisbook also give new research trends in deep learning and optimization for imaging science. It should bea good reference for researchers working on related problems, as well as for researchers working oncomputer vision and visualization, inverse problems, image processing and medical imaging..
出版日期Conference proceedings 2021
关键词Low-Rank Matrix Reconstruction; Non-Convex Methods; Image Selective Segmentation Models; Variational In
版次1
doihttps://doi.org/10.1007/978-981-16-2701-9
isbn_softcover978-981-16-2703-3
isbn_ebook978-981-16-2701-9Series ISSN 2194-1009 Series E-ISSN 2194-1017
issn_series 2194-1009
copyrightSpringer Nature Singapore Pte Ltd. 2021
The information of publication is updating

书目名称Mathematical Methods in Image Processing and Inverse Problems影响因子(影响力)




书目名称Mathematical Methods in Image Processing and Inverse Problems影响因子(影响力)学科排名




书目名称Mathematical Methods in Image Processing and Inverse Problems网络公开度




书目名称Mathematical Methods in Image Processing and Inverse Problems网络公开度学科排名




书目名称Mathematical Methods in Image Processing and Inverse Problems被引频次




书目名称Mathematical Methods in Image Processing and Inverse Problems被引频次学科排名




书目名称Mathematical Methods in Image Processing and Inverse Problems年度引用




书目名称Mathematical Methods in Image Processing and Inverse Problems年度引用学科排名




书目名称Mathematical Methods in Image Processing and Inverse Problems读者反馈




书目名称Mathematical Methods in Image Processing and Inverse Problems读者反馈学科排名




单选投票, 共有 1 人参与投票
 

0票 0.00%

Perfect with Aesthetics

 

1票 100.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 21:55:04 | 显示全部楼层
发表于 2025-3-22 02:55:41 | 显示全部楼层
发表于 2025-3-22 06:05:43 | 显示全部楼层
发表于 2025-3-22 11:14:09 | 显示全部楼层
发表于 2025-3-22 13:14:52 | 显示全部楼层
A New Initialization Method for Neural Networks with Weight Sharing, initialization. In this paper we will propose a new initialization method which will increase training speed and training stability of neural networks with heavy weight sharing. We will also propose a simple yet efficient method to adjust learning rates layer by layer which is indispensable to our initialization.
发表于 2025-3-22 20:24:18 | 显示全部楼层
The Shortest Path AMID 3-D Polyhedral Obstacles,We use the gradient descent method in conjunction with Intermittent Diffusion (ID), a global optimization strategy, to deduce SDEs for the globally optimal solution. Compared to the existing methods, our algorithm is efficient, easier to implement, and able to obtain the solution with any desirable precisions.
发表于 2025-3-23 00:50:08 | 显示全部楼层
发表于 2025-3-23 01:34:04 | 显示全部楼层
发表于 2025-3-23 09:09:40 | 显示全部楼层
A Total Variation Regularization Method for Inverse Source Problem with Uniform Noise,le, the optimization problem is further converted into a minimax problem. Then first order primal-dual method is applied to find the saddle point of the minimax problem. Numerical examples are given to demonstrate that our proposed method outperforms the other testing methods.
 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 22:16
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