检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

23 篇 会议
2 册 图书

馆藏范围

25 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

17 篇 理学
- 14 篇 数学
- 2 篇 物理学
- 2 篇 化学
- 2 篇 地球物理学
- 1 篇 地质学
- 1 篇 生物学
16 篇 工学
- 10 篇 计算机科学与技术...
- 6 篇 光学工程
- 5 篇 电气工程
- 4 篇 信息与通信工程
- 4 篇 软件工程
- 2 篇 建筑学
- 2 篇 土木工程
- 2 篇 生物医学工程（可授...
- 1 篇 仪器科学与技术
- 1 篇 电子科学与技术（可...
- 1 篇 地质资源与地质工...
- 1 篇 生物工程
1 篇 医学
- 1 篇 临床医学

主题

3 篇 variational tech...
2 篇 inverse problems
2 篇 computer communi...
2 篇 signal, image an...
2 篇 machine learning
2 篇 mathematics of c...
2 篇 computer imaging...
1 篇 sampled methods
1 篇 musculoskeletal ...
1 篇 super-resolution
1 篇 eigen-analysis
1 篇 scale-space filt...
1 篇 photointerpretat...
1 篇 medical imaging ...
1 篇 geodesy
1 篇 kaczmarz methods
1 篇 diffusion echoes
1 篇 tikhonov regular...
1 篇 magnetic prospec...
1 篇 iterative method...

机构

2 篇 department of ma...
2 篇 department of ma...
2 篇 university of fe...
2 篇 university of ca...
2 篇 university of pi...
2 篇 university of li...
2 篇 queen mary unive...
2 篇 heriot-watt univ...
1 篇 british columbia...
1 篇 ophthalmology de...
1 篇 research station...
1 篇 department of ma...
1 篇 university of bo...
1 篇 fraunhofer insti...
1 篇 science park 123...
1 篇 ceremade cnrs an...
1 篇 mathematical ima...
1 篇 university of bo...
1 篇 simon fraser uni...
1 篇 department of me...

作者

2 篇 carola-bibiane s...
2 篇 sherry finn m.
2 篇 aujol jean-franç...
2 篇 silvia gazzola
2 篇 girometti laura
2 篇 romina gaburro
2 篇 kostas papafitso...
2 篇 tatiana a. bubba
2 篇 duits remco
2 篇 marcelo pereyra
2 篇 bubba tatiana a.
1 篇 schlindwein fabi...
1 篇 ghyselincks simo...
1 篇 ta vinh-thong
1 篇 gaa daniel
1 篇 van den berg nic...
1 篇 traonmilin yann
1 篇 huber richard
1 篇 drew mark s.
1 篇 kloiber julian

语言

25 篇 英文

检索条件"任意字段=10th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2025"

共 25 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

10th international conference on scale space and variational methods in computer vision, ssvm 2025

10th International Conference on Scale Space and Variational...

引用

10th international conference on scale space and variational methods in computer vision, ssvm 2025

ISBN: (纸本)9783031923654

the proceedings contain 63 papers. the special focus in this conference is on scale space and variational methods in computer vision. the topics include: Fast Inexact Bilevel Optimization for Analytical Deep Image Priors;why do we Regularise in Every Iteration for Imaging Inverse Problems?;on an Analytical Inversion Formula for the Modulo Radon Transform;multiResolution Low-Rank Regularization of Dynamic Imaging Problems;a Fractional Graph La+Ψ Approach to Image Reconstruction;a Fractional-Order Telegraph Diffusion Model for Multiplicative Noise Removal;self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems;real-Time Scene Recovery from Image scale space and Perceptual Hue Similarity;a Novel Interpretation of the Radon Transform’s Ray and Pixel-Driven Discretizations Under Balanced Resolutions;Product of Gaussian Mixture Diffusion Model for Non-linear MRI Inversion;Whiteness-Based Bilevel Estimation of Weighted TV Parameter Maps for Image Denoising;Direct Atomistic Reconstruction in Homogeneous Cryo-EM Using Protein Geometry Regularization;enhanced Denoising and Convergent Regularisation Using Tweedie Scaling;max-Sparsity Atomic Autoencoders with Application to Inverse Problems;equivariant Bootstrap for Uncertainty Quantification in Image Classification;equivariant Denoisers for Image Restoration;max-Normalized Radon Cumulative Distribution Transform for Limited Data Classification;prediction of Parametric Surfaces for Multi-object Segmentation in 3D Biological Imaging;plug-and-Play Half-Quadratic Splitting for Ptychography;deep Unrolling for Learning Optimal Spatially Varying Regularisation Parameters for Total Generalised Variation;identifying Memorization of Diffusion Models through p-Laplace Analysis;learning Anisotropic Metrics for Geodesic Distances via the Heat Equation for Image Segmentation;deceptive Diffusion: Generating Synthetic Adversarial Examples;TomoSelfDEQ: Self-supervised Deep Equilibrium Learning for Sparse-Angle C

关键词：

来源：评论

学校读者我要写书评

暂无评论

10th international conference on scale space and variational methods in computer vision, ssvm 2025

10th International Conference on Scale Space and Variational...

引用

10th international conference on scale space and variational methods in computer vision, ssvm 2025

ISBN: (纸本)9783031923685

关键词：

来源：评论

学校读者我要写书评

暂无评论

A variational Method for Curve Extraction 10th

A Variational Method for Curve Extraction

引用

10th international conference on scale space and variational methods in computer vision, ssvm 2025

作者： Arthaud, Majid Chambolle, Antonin Duval, Vincent ENPC 6 Av. Blaise Pascal Champs-sur-Marne77420 France INRIA Mokaplan Paris France CEREMADE CNRS and Université Paris-Dauphine PSL Paris France

ISBN: (纸本)9783031923685

We propose a functional for extracting curves between a list of possible endpoints, based on a discretization of a variational energy and Smirnov’s decomposition theorem for vector fields. It is then used to design a bi-level minimization approach to automatically extract curves and 1D structures from an image, which is mostly unsupervised. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Functional analysis

来源：评论

学校读者我要写书评

暂无评论

Fractional Derivative variational Model for Additive Signal Decomposition 10th

Fractional Derivative Variational Model for Additive Signal...

引用

10th international conference on scale space and variational methods in computer vision, ssvm 2025

作者： Girometti, Laura Lanza, Alessandro Morigi, Serena Department of Mathematics University of Bologna Bologna Italy

ISBN: (纸本)9783031923685

We present a novel variational model for the additive decomposition of 1D noisy signals. the model relies on sparsifying fractional-order derivatives of the sought-for components to capture intricate signal structures. To efficiently solve the resulting optimization problem, an alternating direction method of multipliers-based algorithm is developed. Furthermore, a bilevel optimization framework is proposed to automatically select "optimal" values of all the free parameters in the model, including the orders of the sparsified fractional derivatives. Preliminary results validate the effectiveness of the proposed approach in accurately decomposing noisy signals, even in the presence of abrupt changes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： variational techniques

来源：评论

学校读者我要写书评

暂无评论

Multigrid methods for Total Variation 10th

Multigrid Methods for Total Variation

引用

10th international conference on scale space and variational methods in computer vision, ssvm 2025

作者： Guerra, Felipe Valkonen, Tuomo Quito Ecuador Department of Mathematics Escuela Politécnica Nacional Quito Ecuador Department of Mathematics and Statistics University of Helsinki Helsinki Finland

ISBN: (纸本)9783031923685

Based on a nonsmooth coherence condition, we construct and prove the convergence of a forward-backward splitting method that alternates between steps on a fine and a coarse grid. Our focus is on total variation regularised inverse imaging problems, specifically, their dual problems, for which we develop in detail the relevant coarse-grid problems. We demonstrate the performance of our method on total variation denoising and magnetic resonance imaging. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

Real-Time Scene Recovery from Image scale space and Perceptual Hue Similarity 10th

Real-Time Scene Recovery from Image Scale Space and Percep...

引用

10th international conference on scale space and variational methods in computer vision, ssvm 2025

作者： He, Roy Y. Wang, Han City University of Hong Kong Kowloon Tong Hong Kong

ISBN: (纸本)9783031923654

Chromatic degradation is often caused by multiple factors that are difficult to retrieve after images have been acquired, making scene recovery a highly ill-posed inverse problem. In this paper, we leverage the nature of human vision and propose an effective algorithm to achieve robust and visually appealing reconstructions. Specifically, we develop a reliable method for estimating chromatic bias by averaging colors from large, blurred regions identified through scale-space analysis. We then utilize CIELAB perceptual hue similarity to determine the degree of degradation caused by transmission and backscattering. By combining these estimators, we recover the scene by adaptively discounting the bias across the image domain. We conduct numerical experiments and comparative studies to demonstrate the effectiveness of our method. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

Skeletonisation scale-spaces 10th

Skeletonisation Scale-Spaces

引用

10th international conference on scale space and variational methods in computer vision, ssvm 2025

作者： Gierke, Julia Peter, Pascal Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Campus E1.7 Saarland University Saarbrücken66041 Germany

ISBN: (纸本)9783031923685

the medial axis transform is a well-known tool for shape recognition. Instead of the object contour, it equivalently describes a binary object in terms of a skeleton containing all centres of maximal inscribed discs. While this shape descriptor is useful for many applications, it is also sensitive to noise: Small boundary perturbations can result in large unwanted expansions of the skeleton. Pruning offers a remedy by removing unwanted skeleton parts. In our contribution, we generalise this principle to skeleton sparsification: We show that subsequently removing parts of the skeleton simplifies the associated shape in a hierarchical manner that obeys scale-space properties. To this end, we provide both a continuous and discrete theory that incorporates architectural and simplification statements as well as equivariances. We illustrate how our skeletonisation scale-spaces can be employed for practical applications with two proof-of-concept implementations for pruning and compression. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

Parameter-Free Structure-Texture Image Decomposition by Unrolling 10th

Parameter-Free Structure-Texture Image Decomposition by Unr...

引用

10th international conference on scale space and variational methods in computer vision, ssvm 2025

作者： Girometti, Laura Aujol, Jean-François Guennec, Antoine Traonmilin, Yann Department of Mathematics University of Bologna Bologna Italy University of Bordeaux Bordeaux INP CNRS IMB UMR 5251 TalenceF-33400 France

ISBN: (纸本)9783031923654

In this work, we propose a parameter-free and efficient method to tackle the structure-texture image decomposition problem. In particular, we present a neural network LPR-NET based on the unrolling of the Low Patch Rank model. On the one hand, this allows us to automatically learn parameters from data, and on the other hand to be computationally faster while obtaining qualitatively similar results compared to traditional iterative model-based methods. Moreover, despite being trained on synthetic images, numerical experiments show the ability of our network to generalize well when applied to natural images. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Inversion of Magnetic Data Using Learned Dictionaries and scale space

Inversion of Magnetic Data Using Learned Dictionaries and ...

引用

10th international conference on scale space and variational methods in computer vision, ssvm 2025

作者： Ahamed, Shadab Ghyselincks, Simon Arias, Pablo Chang Huang Kloiber, Julian Ranjbar, Yasin Tang, Jingrong Zakariaei, Niloufar Haber, Eldad Department of Physics and Astronomy University of British Columbia VancouverBC Canada Department of Earth Ocean and Atmospheric Sciences University of British Columbia VancouverBC Canada Department of Mechanical Engineering University of British Columbia VancouverBC Canada

ISBN: (纸本)9783031923685

Magnetic data inversion is an important tool in geophysics, used to infer subsurface magnetic susceptibility distributions from surface magnetic field measurements. this inverse problem is inherently ill-posed, characterized by non-unique solutions, depth ambiguity, and sensitivity to noise. Traditional inversion approaches rely on predefined regularization techniques to stabilize solutions, limiting their adaptability to complex or diverse geological scenarios. In this study, we propose an approach that integrates variable dictionary learning and scale-space methods to address these challenges. Our method employs learned dictionaries, allowing for adaptive representation of complex subsurface features that are difficult to capture with predefined bases. Additionally, we extend classical variational inversion by incorporating multi-scale representations through a scale-space framework, enabling the progressive introduction of structural detail while mitigating overfitting. We implement both fixed and dynamic dictionary learning techniques, with the latter introducing iteration-dependent dictionaries for enhanced flexibility. Using a synthetic dataset to simulate geological scenarios, we demonstrate significant improvements in reconstruction accuracy and robustness compared to conventional variational and dictionary-based methods. Our results highlight the potential of learned dictionaries, especially when coupled with scale-space dynamics, to improve model recovery and noise handling. these findings underscore the promise of our data-driven approach for advance magnetic data inversion and its applications in geophysical exploration, environmental assessment, and mineral prospecting. the code is publicly available at: https://***/ahxmeds/***. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic prospecting

来源：评论

学校读者我要写书评

暂无评论

the variational Approach to the Flow of Sobolev-Diffeomorphisms Model 9th

The Variational Approach to the Flow of Sobolev-Diffeomor...

引用

9th international conference on scale space and variational methods in computer vision, ssvm 2023

作者： Guastini, Mara Rajković, Marko Rumpf, Martin Wirth, Benedikt Institute for Numerical Simulation University of Bonn Endenicher Allee 60 Bonn53115 Germany Applied Mathematics Münster University of Münster Orléans-Ring 10 Münster48149 Germany

ISBN: (纸本)9783031319747

the flow of diffeomorphisms, aka LDDMM, is a framework to define a group G of diffeomorphisms of chosen regularity with a Riemannian structure. If these diffeomorphisms are used to deform a template shape or image, they generate a space of shapes or images to which the Riemannian structure descends. For this reason LDDMM lies at the centre of computational anatomy, shape space theory, and image metamorphosis. Typically, to obtain a geodesic equation on G one formally applies the geodesic equation of Riemannian Lie groups, to which G has structural similarity. then interpolation tasks between two given deformations, needed for all kinds of statistical analyses, are solved by shooting discretized geodesics within an optimal control approach. If G is chosen with Sobolev regularity, it is known to be a veritable infinite-dimensional Riemannian manifold. In this setting we derive the weak geodesic PDE, which in its strong form coincides with the formally derived one, and present a time discretization to compute a geodesic between given deformations by minimizing a time-discrete path energy, which Mosco-converges to the continuous path energy. this variational ansatz is a more natural alternative to shooting and to our knowledge the first numerical approach to LDDMM by minimization. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Geodesy

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共3页 << < 1 2 3 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：