• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种基于水平集的几何约束与数据逼近下的图像分割模型。

A Level Set-Based Model for Image Segmentation under Geometric Constraints and Data Approximation.

作者信息

Khayretdinova Guzel, Apprato Dominique, Gout Christian

机构信息

National Institute for Applied Sciences (INSA Rouen), Laboratoire de Mathématiques de l'INSA, 76000 Rouen, France.

Tomsk State University of Control Systems and Radioelectronics (TUSUR), Television and Control, 634050 Tomsk, Russia.

出版信息

J Imaging. 2023 Dec 22;10(1):2. doi: 10.3390/jimaging10010002.

DOI:10.3390/jimaging10010002
PMID:38248987
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10816950/
Abstract

In this paper, we propose a new model for image segmentation under geometric constraints. We define the geometric constraints and we give a minimization problem leading to a variational equation. This new model based on a minimal surface makes it possible to consider many different applications from image segmentation to data approximation.

摘要

在本文中，我们提出了一种用于几何约束下图像分割的新模型。我们定义了几何约束，并给出了一个导致变分方程的最小化问题。这种基于极小曲面的新模型使得考虑从图像分割到数据逼近等许多不同的应用成为可能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/632dabbf41e1/jimaging-10-00002-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/ec1db84fb4ab/jimaging-10-00002-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/ec260ae4c1ed/jimaging-10-00002-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/e5dcf35f12f3/jimaging-10-00002-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/38eef983513f/jimaging-10-00002-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/d14c83d5f165/jimaging-10-00002-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/0975b07007d8/jimaging-10-00002-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/dbd14830d108/jimaging-10-00002-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/064a67b56ef1/jimaging-10-00002-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/ca86fbce6a6d/jimaging-10-00002-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/467f312f3979/jimaging-10-00002-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/19f2eadc235b/jimaging-10-00002-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/8055e5e67867/jimaging-10-00002-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/632dabbf41e1/jimaging-10-00002-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/ec1db84fb4ab/jimaging-10-00002-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/ec260ae4c1ed/jimaging-10-00002-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/e5dcf35f12f3/jimaging-10-00002-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/38eef983513f/jimaging-10-00002-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/d14c83d5f165/jimaging-10-00002-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/0975b07007d8/jimaging-10-00002-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/dbd14830d108/jimaging-10-00002-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/064a67b56ef1/jimaging-10-00002-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/ca86fbce6a6d/jimaging-10-00002-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/467f312f3979/jimaging-10-00002-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/19f2eadc235b/jimaging-10-00002-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/8055e5e67867/jimaging-10-00002-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6881/10816950/632dabbf41e1/jimaging-10-00002-g013.jpg

相似文献

1
A Level Set-Based Model for Image Segmentation under Geometric Constraints and Data Approximation.一种基于水平集的几何约束与数据逼近下的图像分割模型。
J Imaging. 2023 Dec 22;10(1):2. doi: 10.3390/jimaging10010002.
2
Geometric flow approach for region-based image segmentation.基于区域的图像分割的几何流方法。
IEEE Trans Image Process. 2012 Dec;21(12):4735-45. doi: 10.1109/TIP.2012.2210724. Epub 2012 Jul 30.
3
Image Segmentation With Eigenfunctions of an Anisotropic Diffusion Operator.基于各向异性扩散算子本征函数的图像分割。
IEEE Trans Image Process. 2016 May;25(5):2155-67. doi: 10.1109/TIP.2016.2541924. Epub 2016 Mar 14.
4
Bi-planar image segmentation based on variational geometrical active contours with shape priors.基于形状先验的双平面图像变分几何主动轮廓分割。
Med Image Anal. 2013 Feb;17(2):165-81. doi: 10.1016/j.media.2012.09.006. Epub 2012 Oct 26.
5
Weakly Supervised Part Proposal Segmentation From Multiple Images.从多幅图像中进行弱监督部分提议分割。
IEEE Trans Image Process. 2017 Aug;26(8):4019-4031. doi: 10.1109/TIP.2017.2708839. Epub 2017 May 26.
6
Variational exemplar-based image colorization.变分范例基图像着色。
IEEE Trans Image Process. 2014 Jan;23(1):298-307. doi: 10.1109/TIP.2013.2288929.
7
Minimization of region-scalable fitting energy for image segmentation.用于图像分割的区域可缩放拟合能量最小化
IEEE Trans Image Process. 2008 Oct;17(10):1940-9. doi: 10.1109/TIP.2008.2002304.
8
Variational image segmentation using boundary functions.基于边界函数的变分图像分割。
IEEE Trans Image Process. 1998;7(9):1269-82. doi: 10.1109/83.709660.
9
Supervised variational model with statistical inference and its application in medical image segmentation.具有统计推断的监督变分模型及其在医学图像分割中的应用。
IEEE Trans Biomed Eng. 2015 Jan;62(1):196-207. doi: 10.1109/TBME.2014.2344660. Epub 2014 Jul 31.
10
Harmonic active contours.谐波活动轮廓。
IEEE Trans Image Process. 2014 Jan;23(1):69-82. doi: 10.1109/TIP.2013.2286326. Epub 2013 Oct 18.

引用本文的文献

1
FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images.FishSegSSL：一种用于鱼眼图像的半监督语义分割框架。
J Imaging. 2024 Mar 15;10(3):71. doi: 10.3390/jimaging10030071.

本文引用的文献

1
Every Inch of the Seafloor: High-tech mapping is finding surprising underwater formations everywhere.
Sci Am. 2022 Aug 1;327(2):40. doi: 10.1038/scientificamerican0822-40.
2
Image Segmentation Using Deep Learning: A Survey.基于深度学习的图像分割技术综述。
IEEE Trans Pattern Anal Mach Intell. 2022 Jul;44(7):3523-3542. doi: 10.1109/TPAMI.2021.3059968. Epub 2022 Jun 3.
3
Image Segmentation for Intensity Inhomogeneity in Presence of High Noise.存在高强度噪声时的强度不均匀性图像分割。
IEEE Trans Image Process. 2018 Aug;27(8):3729-3738. doi: 10.1109/TIP.2018.2825101.
4
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).多模态脑肿瘤图像分割基准（BRATS）。
IEEE Trans Med Imaging. 2015 Oct;34(10):1993-2024. doi: 10.1109/TMI.2014.2377694. Epub 2014 Dec 4.
5
Active contours without edges.无边缘活动轮廓。
IEEE Trans Image Process. 2001;10(2):266-77. doi: 10.1109/83.902291.