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共 291 条记录，以下是31-40 订阅

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Learning Posterior Distributions in Underdetermined Inverse Problems 9th

Learning Posterior Distributions in Underdetermined Inverse...

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

作者： Runkel, Christina Moeller, Michael Schönlieb, Carola-Bibiane Etmann, Christian Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science University of Siegen Siegen Germany

ISBN: (纸本)9783031319747

In recent years, classical knowledge-driven approaches for inverse problems have been complemented by data-driven methods exploiting the power of machine and especially deep learning. Purely data-driven methods, however, come with the drawback of disregarding prior knowledge of the problem even though it has shown to be beneficial to incorporate this knowledge into the problem-solving process. We thus introduce an unpaired learning approach for learning posterior distributions of underdetermined inverse problems. It combines advantages of deep generative modeling with established ideas of knowledge-driven approaches by incorporating prior information about the inverse problem. We develop a new neural network architecture ’UnDimFlow’ (short for Unequal Dimensionality Flow) consisting of two normalizing flows, one from the data to the latent, and one from the latent to the solution space. Additionally, we incorporate the forward operator to develop an unpaired learning method for the UnDimFlow architecture and propose a tailored point estimator to recover an optimal solution during inference. We evaluate our method on the two underdetermined inverse problems of image inpainting and super-resolution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Inverse problems

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Skeletonisation scale-spaces 10th

Skeletonisation Scale-Spaces

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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

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Geometric Adaptations of PDE-G-CNNs 9th

Geometric Adaptations of PDE-G-CNNs

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

作者： Bellaard, Gijs Pai, Gautam Bescos, Javier Olivan Duits, Remco Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Philips Eindhoven Netherlands

ISBN: (纸本)9783031319747

Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. The recently introduced framework of PDE-based G-CNNs (PDE-G-CNNs) generalizes G-CNNs while simultaneously reducing network complexity and increasing performance. In PDE-G-CNNs the usual building blocks of neural networks are replaced with solvers for evolution PDEs, these PDEs being convection, diffusion, dilation, and erosion. We investigate three geometric adaptations of PDE-G-CNNs: We generalize the theory in [2] to a family of Lie groups in between roto-translation group SE(2) and Heisenberg group H(3). This geometric adaptation enables transferring training orientation score processing on SE(2) to training processing of velocity scores, shearlet transforms, or frequency scores on H(3).We theoretically prove that the trainable lifting layer in a PDE-G-CNN is interchangeable with a single fixed untrained lifting coupled with multiple trainable convections. We experimentally validate this theoretical insight and report identical performance. This fixing of the lifting layer makes PDE-G-CNNs more interpretable as they now solely train association fields from *** include curvature adaptation in PDE-G-CNNs. This curvature adaptation is beneficial within the convection part of PDE-G-CNNs as we show experimentally. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Lie groups

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Multigrid methods for Total Variation 10th

Multigrid Methods for Total Variation

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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

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Collation of Face Recognition Techniques: Image Registration and Dimension Reduction Approaches

Collation of Face Recognition Techniques: Image Registration...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Gaurav Gupta Anuj Kumar Goel Computer Science Engineering Department Chandigarh University Apex Institute of Technology Mohali Punjab India Electronics and Communication Engineering Department Chandigarh University Chandigarh University Mohali Punjab India

Faces must be present in images for intelligent vision-based human computer interaction to work. Since many years ago, face recognition research has been ongoing and significant. This procedure entails facial tracking, expression recognition, and many other things. It is crucial to first register the positions of the photos in this process. However, maintaining such a database is highly challenging because of pose invariance, illumination invariance, shift invariance, scale and noise invariance. The Image Registration Algorithm will be used to register all of the unknown photos that are currently in the database. With the use of a reference, this registration technique tries to align a pattern image. This algorithm will produce features that are independent of translation, scaling and rotation. High dimension space, which must be decreased using a dimension reduction algorithm, is the primary challenge in face recognition. This low dimension subspace can be produced using dimension reduction methods like as Linear Discriminant Analysis, Principal Component Analysis and Locality Preserving Projection. In order to find the needed image from the given database, this study will first minimize the dimensions of every image that is now there.

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Appropriate Order of Regularization in 3D/2D Image Registration 10th

Appropriate Order of Regularization in 3D/2D Image Registr...

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

作者： Schulz, Pia F. Mannel, Florian Modersitzki, Jan Institute of Mathematics and Image Computing University of Lübeck Lübeck23562 Germany Fraunhofer Institute for Digital Medicine MEVIS Lübeck23562 Germany

ISBN: (纸本)9783031923685

This paper pertains to the order of regularization in variational 3D/2D image registration. Such registration problems often occur in image-guided interventions, e.g. in ardiology, neurology, or orthopedics. In order to align 3D pre-operative data with 2D data acquired during the intervention, a 3D deformation is calculated. A common approach is to minimize an energy that consists of a data fitting and a regularization term. While the first is evaluated on a 2D set, the regularization acts on a 3D domain. Thus, the regularizer has to ensure that the information given on the 2D set is transferred to the 3D domain. Therefore, the regularization should be chosen such that the resulting 3D deformation is sufficiently smooth, e.g. continuously differentiable. This work addresses the question which regularization order is required to guarantee continuously differentiable deformations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： variational techniques

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Inverse scale space Iterations for Non-convex variational Problems Using Functional Lifting 8th

Inverse Scale Space Iterations for Non-convex Variational Pr...

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

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

ISBN: (纸本)9783030755485

Non-linear filtering approaches allow to obtain decompositions of images with respect to a non-classical notion of scale. The associated inverse scale space flow can be obtained using the classical Bregman iteration applied to a convex, absolutely one-homogeneous regularizer. In order to extend these approaches to general energies with non-convex data term, we apply the Bregman iteration to a lifted version of the functional with sublabel-accurate discretization. We provide a condition for the subgradients of the regularizer under which this lifted iteration reduces to the standard Bregman iteration. We show experimental results for the convex and non-convex case. © 2021, Springer Nature Switzerland AG.

关键词： Inverse problems

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

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

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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

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Fluctuation-Based Deconvolution in Fluorescence Microscopy Using Plug-and-Play Denoisers 9th

Fluctuation-Based Deconvolution in Fluorescence Microscopy ...

引用

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

作者： Stergiopoulou, Vasiliki Mukherjee, Subhadip Calatroni, Luca Blanc-Féraud, Laure UCA CNRS INRIA Laboratoire I3S Sophia-Antipolis06903 France Department of Computer Science University of Bath Bath United Kingdom

ISBN: (纸本)9783031319747

The spatial resolution of images of living samples obtained by fluorescence microscopes is physically limited due to the diffraction of visible light, which makes the study of entities of size less than the diffraction barrier (around 200 nm in the x-y plane) very challenging. To overcome this limitation, several deconvolution and super-resolution techniques have been proposed. Within the framework of inverse problems, modern approaches in fluorescence microscopy reconstruct a super-resolved image from a temporal stack of frames by carefully designing suitable hand-crafted sparsity-promoting regularisers. Numerically, such approaches are solved by proximal gradient-based iterative schemes. Aiming at obtaining a reconstruction more adapted to sample geometries (e.g. thin filaments), we adopt a plug-and-play denoising approach with convergence guarantees and replace the proximity operator associated with the explicit image regulariser with an image denoiser (i.e. a pre-trained network) which, upon appropriate training, mimics the action of an implicit prior. To account for the independence of the fluctuations between molecules, the model relies on second-order statistics. The denoiser is then trained on covariance images coming from data representing sequences of fluctuating fluorescent molecules with filament structure. The method is evaluated on both simulated and real fluorescence microscopy images, showing its ability to correctly reconstruct filament structures with high values of peak signal-to-noise ratio (PSNR). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fluorescence microscopy

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Linear PDE Osmotic Flow for 3D Surfaces 10th

Linear PDE Osmotic Flow for 3D Surfaces

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

作者： Huska, Martin Morigi, Serena Recupero, Giuseppe Antonio Department of Mathematics University of Bologna BO Bologna40126 Italy

ISBN: (纸本)9783031923685

We present a proof-of-concept study demonstrating the application of the linear osmotic flow to unstructured domains in R3, such as irregular meshes. This study aims to extend the osmotic filter to surfaces, thereby simplifying some geometry processing tasks, such as surface inpainting and completion (cloning), that typically require costly and complex algorithms. This will include two new challenges: the numerical solution of the drift-diffusion osmotic model on meshes, and the use of surface geometric descriptors, which play the role of reference function for the osmotic flow. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electroosmosis

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