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检索条件"任意字段=3rd International Conference on Scale Space and Variational Methods in Computer Vision"

共 291 条记录，以下是41-50 订阅

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Diffusion-Shock Filtering on the space of Positions and Orientations 10th

Diffusion-Shock Filtering on the Space of Positions and ...

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

作者： Sherry, Finn M. Schaefer, Kristina Duits, Remco CASA & EAISI Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Mathematical Image Analysis Group Department of Mathematics and Computer Science Saarland University E1.7 Saarbrücken66123 Germany

ISBN: (纸本)9783031923685

We extend Regularised Diffusion-Shock (rdS) filtering from Euclidean space R2 [26] to the space of positions and orientations M2:=R2×S1. This has numerous advantages, e.g. making it possible to enhance and inpaint crossing structures, since they become disentangled when lifted to M2. We create a version of the algorithm using gauge frames to mitigate issues caused by lifting to a finite number of orientations. This leads us to study generalisations of diffusion, since the gauge frame diffusion is not generated by the Laplace-Beltrami operator. rdS filtering compares favourably to existing techniques such as Total Roto-Translational Variation (TR-TV) flow [9, 29], NLM [8], and BM3D [13] when denoising images with crossing structures, particularly if they are segmented. Additionally, we see that M2 rdS inpainting is indeed able to restore crossing structures, unlike R2 rdS inpainting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Crossing-Preserving Denoising Diffusion Shock Filter

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Sparsity-Aided variational Mesh Restoration 8th

Sparsity-Aided Variational Mesh Restoration

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8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Huska, Martin Morigi, Serena Recupero, Giuseppe Antonio Department of Mathematics University of Bologna Bologna BO40126 Italy

ISBN: (纸本)9783030755485

We propose a variational method for recovering discrete surfaces from noisy observations which promotes sparsity in the normal variation more accurately than 1 norm (total variation) and 0 pseudo-norm regularization methods by incorporating a parameterized non-convex penalty function. This results in denoised surfaces with enhanced flat regions and maximally preserved sharp features, including edges and corners. Unlike the classical two-steps mesh denoising approaches, we propose a unique, effective optimization model which is efficiently solved by an instance of Alternating Direction Method of Multipliers. Experiments are presented which strongly indicate that using the sparsity-aided formulation holds the potential for accurate restorations even in the presence of high noise. © 2021, Springer Nature Switzerland AG.

关键词： Restoration

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Bundle scale spaces and Local Gauge Symmetries for Graph Networks 10th

Bundle Scale Spaces and Local Gauge Symmetries for Graph N...

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

作者： Cassel, Jonas Schlindwein, Fabio Albers, Peter Schnörr, Christoph Institute for Mathematics Heidelberg University Heidelberg Germany Research Station Geometry and Dynamics Heidelberg University Heidelberg Germany

ISBN: (纸本)9783031923685

We introduce a class of processing architectures for node features on a graph, that are equivariant with respect to local actions of a general symmetry group G, i.e. G may act on each feature φv at some node v by an individual transformation gv∈G. Our method is based on modelling node feature data in terms of sections Γ(E) of associated vector bundles, which ensures preservation of local symmetries by construction. Processing architectures are then derived from mappings of bundle sections F:Γ(E)→Γ(E). We focus on mappings F induced by diffusion PDEs on vector bundles associated to a generalized Laplacian. We define a bundle scale space, which contains non-trivial fixed points corresponding to harmonic sections of the associated vector bundle, in contrast to the basic Gaussian scale space. We utilize vector diffusion maps and lattice gauge theory in order to discretize the geometric PDEs such that local symmetries are still respected. We outline parametrizations suited for supervised machine learning scenarios and provide a proof-of-concept numerical experiment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Supervised learning

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Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm 10th

Convergence Analysis of a Proximal Stochastic Denoising Re...

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

作者： Renaud, Marien Hermant, Julien Papadakis, Nicolas Univ. Bordeaux CNRS INRIA Bordeaux INP IMB UMR 5251 Talence33400 France

ISBN: (纸本)9783031923685

Plug-and-Play methods for image restoration are iterative algorithms that solve a variational problem to recover a clean image from a degraded observation. These algorithms are known to be flexible to changes of degradation and to perform state-of-the-art restoration. Recently, significant efforts have been made to explore new stochastic algorithms based on the Plug-and-Play or REgularization by Denoising (RED) frameworks, such as SNORE, which is a convergent stochastic gradient descent algorithm. A variant of this algorithm, named SNORE Prox, reaches state-of-the-art performances, especially for inpainting tasks. However, the convergence of SNORE Prox, that can be seen as a stochastic proximal gradient descent, has not been analyzed so far. In this paper, we prove the convergence of SNORE Prox under non convex assumptions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： variational techniques

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A Novel Interpretation of the Radon Transform’s Ray and Pixel-Driven Discretizations Under Balanced Resolutions 10th

A Novel Interpretation of the Radon Transform’s Ray and ...

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

作者： Huber, Richard Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark

ISBN: (纸本)9783031923654

Tomographic investigations are a central tool in medical applications, allowing doctors to image the interior of patients. The corresponding measurement process is commonly modeled by the Radon transform. In practice, the solution of the tomographic problem requires discretization of the Radon transform and its adjoint (called the backprojection). There are various discretization schemes;often structured around three discretization parameters: spatial-, detector-, and angular resolutions. The most widespread approach uses the ray-driven Radon transform and the pixel-driven backprojection in a balanced resolution setting, i.e., the spatial resolution roughly equals the detector resolution. The use of these particular discretization approaches is based on anecdotal reports of their approximation performance, but there is little rigorous analysis of these methods’ approximation errors. This paper presents a novel interpretation of ray-driven and pixel-driven methods as convolutional discretizations, illustrating that from an abstract perspective these methods are similar. Moreover, we announce statements concerning the convergence of the ray-driven Radon transform and the pixel-driven backprojection under balanced resolutions. Our considerations are supported by numerical experiments highlighting certain aspects of the discussed methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Error analysis

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scale-Covariant and scale-Invariant Gaussian Derivative Networks 8th

Scale-Covariant and Scale-Invariant Gaussian Derivative Netw...

引用

8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Lindeberg, Tony Computational Brain Science Lab Division of Computational Science and Technology KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9783030755485

This paper presents a hybrid approach between scale-space theory and deep learning, where a deep learning architecture is constructed by coupling parameterized scale-space operations in cascade. By sharing the learnt parameters between multiple scale channels, and by using the transformation properties of the scale-space primitives under scaling transformations, the resulting network becomes provably scale covariant. By in addition performing max pooling over the multiple scale channels, a resulting network architecture for image classification also becomes provably scale invariant. We investigate the performance of such networks on the MNISTLargescale dataset, which contains rescaled images from original MNIST over a factor of 4 concerning training data and over a factor of 16 concerning testing data. It is demonstrated that the resulting approach allows for scale generalization, enabling good performance for classifying patterns at scales not present in the training data. © 2021, Springer Nature Switzerland AG.

关键词： Network architecture

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Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy 8th

Total Deep Variation for Noisy Exit Wave Reconstruction in T...

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8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Pinetz, Thomas Kobler, Erich Doberstein, Christian Berkels, Benjamin Effland, Alexander Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Austrian Institute of Technology Vienna Austria AICES Graduate School RWTH Aachen University Aachen Germany Graz Austria Institute for Applied Mathematics University of Bonn Bonn Germany

ISBN: (纸本)9783030755485

Transmission electron microscopes (TEMs) are ubiquitous devices for high-resolution imaging on an atomic level. A key problem related to TEMs is the reconstruction of the exit wave, which is the electron signal at the exit plane of the examined specimen. Frequently, this reconstruction is cast as an ill-posed nonlinear inverse problem. In this work, we integrate the data-driven total deep variation regularizer to reconstruct the exit wave in this inverse problem. In several numerical experiments, the applicability of the proposed method is demonstrated for different materials. © 2021, Springer Nature Switzerland AG.

关键词： Numerical methods

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variational Models for Signal Processing with Graph Neural Networks 8th

Variational Models for Signal Processing with Graph Neural N...

引用

8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Azad, Amitoz Rabin, Julien Elmoataz, Abderrahim Normandie Univ UNICAEN ENSICAEN CNRS GREYC Caen14000 France

ISBN: (纸本)9783030755485

This paper is devoted to signal processing on point-clouds by means of neural networks. Nowadays, state-of-the-art in image processing and computer vision is mostly based on training deep convolutional neural networks on large datasets. While it is also the case for the processing of point-clouds with Graph Neural Networks (GNN), the focus has been largely given to high-level tasks such as classification and segmentation using supervised learning on labeled datasets such as ShapeNet. Yet, such datasets are scarce and time-consuming to build depending on the target application. In this work, we investigate the use of variational models for such GNN to process signals on graphs for unsupervised learning. Our contributions are two-fold. We first show that some existing variational - based algorithms for signals on graphs can be formulated as Message Passing Networks (MPN), a particular instance of GNN, making them computationally efficient in practice when compared to standard gradient-based machine learning algorithms. Secondly, we investigate the unsupervised learning of feed-forward GNN, either by direct optimization of an inverse problem or by model distillation from variational-based MPN. © 2021, Springer Nature Switzerland AG.

关键词： Graph neural networks

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Prediction of Parametric Surfaces for Multi-object Segmentation in 3D Biological Imaging 10th

Prediction of Parametric Surfaces for Multi-object Segment...

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

作者： Rapilly, Quentin Badoual, Anaïs Maindron, Pierre Bouet, Guenaelle Kervrann, Charles Centre Inria de l’Université de Rennes Equipe SAIRPICO Unité Chimie et Biologie de la Cellule U1143 INSERM UMR3666 CNRS Institut Curie Université PSL Rennes France Mines Saint Etienne Université Jean Monnet Saint-Étienne INSERM SAINBIOSE U1059 Saint-Étienne42023 France

ISBN: (纸本)9783031923654

Multi-object segmentation algorithms are of great interest in a very large range of fields. Deep learning brought major improvements in terms of processing speed or prediction accuracy. Nevertheless, some traditional methods such as active surfaces have features that conventional deep learning methods cannot provide, especially representing the object in a continuous geometrical way and encoding prior information on the shapes to segment. Those features are of particular interest in biology to efficiently segment noisy and poorly resolved data, and then understand the interactions between segmented cells. We introduce NAGINI-3D (N-Active shapes for seGmentINg 3D biological Images), a new hybrid segmentation method dedicated to multi-object segmentation of 3D images that combines the efficiency of deep learning and the powerful representation of active surfaces. We evaluate our method on real and synthetic 3D datasets of fluorescence microscopy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Quantisation scale-spaces 8th

Quantisation Scale-Spaces

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8th international conference on scale space and variational methods in computer vision, SSVM 2021

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

ISBN: (纸本)9783030755485

Recently, sparsification scale-spaces have been obtained as a sequence of inpainted images by gradually removing known image data. Thus, these scale-spaces rely on spatial sparsity. In the present paper, we show that sparsification of the co-domain, the set of admissible grey values, also constitutes scale-spaces with induced hierarchical quantisation techniques. These quantisation scale-spaces are closely tied to information theoretical measures for coding cost, and therefore particularly interesting for inpainting-based compression. Based on this observation, we propose a sparsification algorithm for the grey-value domain that outperforms uniform quantisation as well as classical clustering approaches. © 2021, Springer Nature Switzerland AG.

关键词： Information theory

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