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

79 篇 会议
3 册 图书
2 篇 期刊文献

馆藏范围

84 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

76 篇 工学
- 63 篇 计算机科学与技术...
- 35 篇 软件工程
- 24 篇 光学工程
- 11 篇 信息与通信工程
- 10 篇 生物工程
- 7 篇 生物医学工程（可授...
- 5 篇 电气工程
- 4 篇 化学工程与技术
- 3 篇 仪器科学与技术
- 3 篇 电子科学与技术（可...
- 3 篇 建筑学
- 3 篇 土木工程
- 2 篇 力学（可授工学、理...
- 2 篇 控制科学与工程
- 1 篇 材料科学与工程（可...
- 1 篇 动力工程及工程热...
- 1 篇 航空宇航科学与技...
- 1 篇 核科学与技术
- 1 篇 安全科学与工程
50 篇 理学
- 36 篇 数学
- 23 篇 物理学
- 10 篇 生物学
- 5 篇 化学
- 4 篇 地球物理学
- 4 篇 统计学（可授理学、...
- 2 篇 系统科学
5 篇 管理学
- 4 篇 图书情报与档案管...
1 篇 法学
- 1 篇 社会学
1 篇 医学
- 1 篇 基础医学(可授医学...
- 1 篇 临床医学

主题

6 篇 variational meth...
4 篇 image segmentati...
4 篇 these keywords w...
3 篇 variational prob...
3 篇 inpainting
3 篇 diffusion filter...
3 篇 pattern recognit...
3 篇 non-linear parti...
3 篇 ginzburg-landau ...
3 篇 image processing...
3 篇 numerical method...
2 篇 image enhancemen...
2 篇 information syst...
2 篇 higher order reg...
2 篇 inverse problems
2 篇 optical flow
2 篇 total variation
2 篇 iterative method...
2 篇 nonlinear spectr...
2 篇 computer graphic...

机构

3 篇 univ innsbruck d...
3 篇 mathematical ima...
3 篇 department of ma...
2 篇 institute of mat...
2 篇 kth royal inst t...
2 篇 department of bi...
2 篇 graz univ math i...
2 篇 univ stuttgart c...
1 篇 department of co...
1 篇 cnrs university ...
1 篇 univ las palmas ...
1 篇 ophthalmology de...
1 篇 department of ap...
1 篇 fraunhofer gesel...
1 篇 technical univer...
1 篇 karlsruhe instit...
1 篇 school of comput...
1 篇 academic center ...
1 篇 hsch wurzburg sc...
1 篇 department of ma...

作者

7 篇 weickert joachim
4 篇 lindeberg tony
3 篇 peter pascal
3 篇 calatroni luca
3 篇 pock thomas
3 篇 grossauer h
3 篇 durou jean-denis
3 篇 cohen laurent d.
3 篇 duits remco
3 篇 kimmel ron
3 篇 scherzer o
2 篇 schnörr c
2 篇 scherzer otmar
2 篇 bruhn andres
2 篇 kuijper arjan
2 篇 bruckstein alfre...
2 篇 maurer daniel
2 篇 schnörr christop...
2 篇 lellmann jan
2 篇 petra stefania

语言

84 篇 英文

检索条件"任意字段=4th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2013"

共 84 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：

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

9th International Conference on Scale Space and Variational ...

引用

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

ISBN: (纸本)9783031319747

the proceedings contain 57 papers. the special focus in this conference is on scale space and variational methods in computer vision. the topics include: EmNeF: Neural Fields for Embedded variational Problems in Imaging;genHarris-ResNet: A Rotation Invariant Neural Network Based on Elementary Symmetric Polynomials;compressive Learning of Deep Regularization for Denoising;graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet;learning Posterior Distributions in Underdetermined Inverse Problems;proximal Residual Flows for Bayesian Inverse Problems;a Model is Worth Tens of thousands of Examples;resolution-Invariant Image Classification Based on Fourier Neural Operators;graph Laplacian for Semi-supervised Learning;efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting;a Geometrically Aware Auto-Encoder for Multi-texture Synthesis;Fast Marching Energy CNN;deep Accurate Solver for the Geodesic Problem;deep Image Prior Regularized by Coupled Total Variation for Image Colorization;hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras;latent-space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data;natural Numerical Networks on Directed Graphs in Satellite Image Classification;piece-wise Constant Image Segmentation with a Deep Image Prior Approach;On the Inclusion of Topological Requirements in CNNs for Semantic Segmentation Applied to Radiotherapy;a Relaxed Proximal Gradient Descent Algorithm for Convergent Plug-and-Play with Proximal Denoiser;theoretical Foundations for Pseudo-Inversion of Nonlinear Operators;off-the-Grid Charge Algorithm for Curve Reconstruction in Inverse Problems;convergence Guarantees of Overparametrized Wide Deep Inverse Prior;On the Remarkable Efficiency of SMART;wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line;a Quasi-Newton Primal-Dual Algorithm with Line Search;stochastic Gradient D

关键词：

来源：评论

学校读者我要写书评

暂无评论

EmNeF: Neural Fields for Embedded variational Problems in Imaging 9th

EmNeF: Neural Fields for Embedded Variational Problems in ...

引用

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

作者： Bednarski, Danielle Lellmann, Jan Institute of Mathematics and Image Computing University of Lübeck Lübeck Germany

ISBN: (纸本)9783031319747

We propose a model-driven neural fields approach for solving variational problems. the approach can be applied to a variety of problems with convex, 1-homogeneous regularizer and arbitrary, possibly non-convex, data term. Our strategy is to embed the non-convex energy into a higher-dimensional space, reaching a convex primal-dual formulation. Instead of using classical gradient-descent based optimization algorithms, we propose training multiple fields representing the primal and dual variables in order to solve the problem. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Gradient methods

来源：评论

学校读者我要写书评

暂无评论

Generalised scale-space Properties for Probabilistic Diffusion Models 9th

Generalised Scale-Space Properties for Probabilistic Diffus...

引用

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

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

ISBN: (纸本)9783031319747

Probabilistic diffusion models enjoy increasing popularity in the deep learning community. they generate convincing samples from a learned distribution of input images with a wide field of practical applications. Originally, these approaches were motivated from drift-diffusion processes, but these origins find less attention in recent, practice-oriented publications. We investigate probabilistic diffusion models from the viewpoint of scale-space research and show that they fulfil generalised scale-space properties on evolving probability distributions. Moreover, we discuss similarities and differences between interpretations of the physical core concept of drift-diffusion in the deep learning and model-based world. To this end, we examine relations of probabilistic diffusion to osmosis filters. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Probability distributions

来源：评论

学校读者我要写书评

暂无评论

Learning Differential Invariants of Planar Curves 9th

Learning Differential Invariants of Planar Curves

引用

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

作者： Velich, Roy Kimmel, Ron Technion - Israel Institute of Technology Haifa Israel

ISBN: (纸本)9783031319747

We propose a learning paradigm for the numerical approximation of differential invariants of planar curves. Deep neural-networks’ (DNNs) universal approximation properties are utilized to estimate geometric measures. the proposed framework is shown to be a preferable alternative to axiomatic constructions. Specifically, we show that DNNs can learn to overcome instabilities and sampling artifacts and produce consistent signatures for curves subject to a given group of transformations in the plane. We compare the proposed schemes to alternative state-of-the-art axiomatic constructions of differential invariants. We evaluate our models qualitatively and quantitatively and propose a benchmark dataset to evaluate approximation models of differential invariants of planar curves. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Learned Discretization Schemes for the Second-Order Total Generalized Variation 9th

Learned Discretization Schemes for the Second-Order Total ...

引用

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

作者： Bogensperger, Lea Chambolle, Antonin Effland, Alexander Pock, thomas Institute of Computer Graphics and Vision Graz University of Technology Graz Austria CEREMADE CNRS & Université Paris-Dauphine PSL Paris France Institute for Applied Mathematics University of Bonn Bonn Germany

ISBN: (纸本)9783031319747

the total generalized variation extends the total variation by incorporating higher-order smoothness. thus, it can also suffer from similar discretization issues related to isotropy. Inspired by the success of novel discretization schemes of the total variation, there has been recent work to improve the second-order total generalized variation discretization, based on the same design idea. In this work, we propose to extend this to a general discretization scheme based on interpolation filters, for which we prove variational consistency. We then describe how to learn these interpolation filters to optimize the discretization for various imaging applications. We illustrate the performance of the method on a synthetic data set as well as for natural image denoising. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

Regularized Material Decomposition for K-edge Separation in Hyperspectral Computed Tomography 9th

Regularized Material Decomposition for K-edge Separation in...

引用

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

作者： Bevilacqua, Francesca Dong, Yiqiu Jørgensen, Jakob Sauer Department of Mathematics University of Bologna Bologna Italy Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

ISBN: (纸本)9783031319747

Hyperspectral computed tomography is a developing technique that exploits the property of materials to attenuate X-rays in different quantities depending on the specific energy. It allows to not only reconstruct the object, but also to estimate the concentration of the materials which compose it. the objective of the present study is to obtain an accurate material decomposition from noisy few-projection data. A preliminary comparative study of reconstruction methods based on material decomposition is performed, employing a phantom composed of materials with similar attenuation profiles with characteristic K-edges separated by only 2, 4 and 6 keV. It is found that a one-stage method encompassing both material decomposition and tomographic reconstruction in a single variational model performs better than a more conventional two-stage approach. It is further found that better modelling of noise through use of a weighted least-squares data fidelity improves reconstruction and material separation, as does the use total variation and L1-norm regularization. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Latent-space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data 9th

Latent-Space Disentanglement with Untrained Generator Netwo...

引用

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

作者： Abdullah, Abdullah Holler, Martin Kunisch, Karl Landman, Malena Sabate Department of Mathematics The Chinese University of Hong Kong Hong Kong Institute of Mathematics and Scientific Computing University of Graz Graz Austria Department of Mathematics Emory University Atlanta United States

ISBN: (纸本)9783031319747

Isolating different types of motion in video data is a highly relevant problem in video analysis. Applications can be found, for example, in dynamic medical or biological imaging, where the analysis and further processing of the dynamics of interest is often complicated by additional, unwanted dynamics, such as motion of the measurement subject. In this work, it is empirically shown that a representation of video data via untrained generator networks, together with a specific technique for latent space disentanglement that uses minimal, one-dimensional information on some of the underlying dynamics, allows to efficiently isolate different, highly non-linear motion types. In particular, such a representation allows to freeze any selection of motion types, and to obtain accurate independent representations of other dynamics of interest. Obtaining such a representation does not require any pre-training on a training data set, i.e., all parameters of the generator network are learned directly from a single video. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

A Frame Decomposition of the Funk-Radon Transform 9th

A Frame Decomposition of the Funk-Radon Transform

引用

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

作者： Quellmalz, Michael Weissinger, Lukas Hubmer, Simon Erchinger, Paul D. Technische Universität Berlin Institute of Mathematics MA 4-3 Straße des 17. Juni 136 Berlin10623 Germany Johann Radon Institute Linz Altenbergerstraße 69 Linz4040 Austria

ISBN: (纸本)9783031319747

the Funk-Radon transform assigns to a function defined on the unit sphere its integrals along all great circles of the sphere. In this paper, we consider a frame decomposition of the Funk-Radon transform, which is a flexible alternative to the singular value decomposition. In particular, we construct a novel frame decomposition based on trigonometric polynomials and show its application for the inversion of the Funk-Radon transform. Our theoretical findings are verified by numerical experiments, which also incorporate a regularization scheme. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

Fast Marching Energy CNN 9th

Fast Marching Energy CNN

引用

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

作者： Bertrand, théo Makaroff, Nicolas Cohen, Laurent D. CEREMADE UMR CNRS 7534 University Paris Dauphine PSL Research University Paris75775 France

ISBN: (纸本)9783031319747

Leveraging geodesic distances and the geometrical information they convey is key for many data-oriented applications in imaging. Geodesic distance computation has been used for long for image segmentation using Image based metrics. We introduce a new method by generating isotropic Riemannian metrics adapted to a problem using CNN and give as illustrations an example of application. We then apply this idea to the segmentation of brain tumours as unit balls for the geodesic distance computed with the metric potential output by a CNN, thus imposing geometrical and topological constraints on the output mask. We show that geodesic distance modules work well in machine learning frameworks and can be used to achieve state-of-the-art performances while ensuring geometrical and/or topological properties. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image segmentation

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9页 << < 1 2 3 4 5 6 7 8 9 > >>

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：