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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61-70 订阅

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

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

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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

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Using the complex Ginzburg-Landau equation for digital inpainting in 2D and 3D

Using the complex Ginzburg-Landau equation for digital inpai...

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4th international conference on scale space methods in computer vision

作者： Grossauer, H Scherzer, O Univ Innsbruck Dept Comp Sci A-6020 Innsbruck Austria

ISBN: (纸本)354040368X

Recently, several different approaches for digital inpainting have been proposed in the literature. We give a review and introduce a novel approach based on the complex Ginzburg-Landau equation. the use of this equation is motivated by some of its remarkable analytical properties. While common inpainting technology is especially designed for restorations of two dimensional image data, the Ginzburg-Landau equation can straight forwardly be applied to restore higher dimensional data, which has applications in frame interpolation, improving sparsely sampled volumetric data and to fill in fragmentary surfaces. the latter application is of importance in architectural heritage preservation. We discuss a stable and efficient scheme for the numerical solution of the Ginzburg-Landau equation and present some numerical experiments. We compare the performance of our algorithm with other well established methods for inpainting.

关键词： Ginzburg-Landau equation inpainting diffusion filtering non-linear partial differential equations variational problems

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New approximation of a scale space kernel on SE(3) and applications in neuroimaging 5

New approximation of a scale space kernel on SE(3) and appli...

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5th international conference on scale space and variational methods in computer vision, ssvm 2015

作者： Portegies, Jorg Sanguinetti, Gonzalo Meesters, Stephan Duits, Remco Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Academic Center for Epileptology Kempenhaeghe and Maastricht UMC+ Heeze Netherlands Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9783319184609

We provide a new, analytic kernel for scale space filtering of dMRI data. the kernel is an approximation for the Green’s function of a hypo-elliptic diffusion on the 3D rigid body motion group SE(3), for fiber enhancement in dMRI. the enhancements are described by linear scale space PDEs in the coupled space of positions and orientations embedded in SE(3). As initial condition for the evolution we use either a Fiber Orientation Distribution (FOD) or an Orientation Density Function (ODF). Explicit formulas for the exact kernel do not exist. Although approximations well-suited for fast implementation have been proposed in literature, they lack important symmetries of the exact kernel. We introduce techniques to include these symmetries in approximations based on the logarithm on SE(3), resulting in an improved kernel. Regarding neuroimaging applications, we apply our enhancement kernel (a) to improve dMRI tractography results and (b) to quantify coherence of obtained streamline bundles. © Springer international Publishing Switzerland 2015.

关键词： Neuroimaging

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

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

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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

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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...

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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

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A Frame Decomposition of the Funk-Radon Transform 9th

A Frame Decomposition of the Funk-Radon Transform

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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

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Fast Marching Energy CNN 9th

Fast Marching Energy CNN

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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

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

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

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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

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Image Blending with Osmosis 9th

Image Blending with Osmosis

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

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

ISBN: (纸本)9783031319747

Image blending is an integral part of many multi-image applications such as panorama stitching or remote image acquisition processes. In such scenarios, multiple images are connected at predefined boundaries to form a larger image. A convincing transition between these boundaries may be challenging, since each image might have been acquired under different conditions or even by different devices. We propose the first blending approach based on osmosis filters. these drift-diffusion processes define an image evolution with a non-trivial steady state. For our blending purposes, we explore several ways to compose drift vector fields based on the derivatives of our input images. these vector fields guide the evolution such that the steady state yields a convincing blended result. Our method benefits from the well-founded theoretical results for osmosis, which include useful invariances under multiplicative changes of the colour values. Experiments on real-world data show that this yields better quality than traditional gradient domain blending, especially under challenging illumination conditions. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Osmosis

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Hypergraph p-Laplacians, scale spaces, and Information Flow in Networks 9th

Hypergraph p-Laplacians, Scale Spaces, and Information Flow...

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

作者： Fazeny, Ariane Tenbrinck, Daniel Burger, Martin Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Deutsches Elektronen-Synchrotron Hamburg22607 Germany Fachbereich Mathematik Universität Hamburg Hamburg20146 Germany

ISBN: (纸本)9783031319747

the aim of this paper is to revisit the definition of differential operators on hypergraphs, which are a natural extension of graphs in systems based on interactions beyond pairs. In particular we focus on the definition of Laplacian and p-Laplace operators, their basic spectral properties, variational structure, and their scale spaces. We shall see that the corresponding gradient flows, i.e., diffusion equations on hypergraphs, are possible models for the information flow on social networks, e.g., in opinion formation based on group discussion. Moreover, the spectral analysis and scale spaces induced by these operators provide a potential method to further analyze complex networks and their multiscale structure. the quest for spectral analysis on hypergraphs motivates in particular a definition of differential operators with trivial first eigenfunction and thus more interpretable second eigenfunctions. this property is not automatically satisfied in existing definitions of hypergraph p-Laplacians and we hence provide a novel axiomatic approach that extends previous definitions and can be specialized to satisfy such (or other) desired properties. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Eigenvalues and eigenfunctions

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