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

ISBN: (纸本)9783030755485

the proceedings contain 45 papers. the special focus in this conference is on scale space and variational methods in computer vision. the topics include: Multiscale Registration;challenges for Optical Flow Estimates in Elastography;an Anisotropic Selection Scheme for variational Optical Flow methods with Order-Adaptive Regularisation;low-Rank Registration of Images Captured Under Unknown, Varying Lighting;towards Efficient Time Stepping for Numerical Shape Correspondence;first Order Locally Orderless Registration;first-Order Geometric Multilevel Optimization for Discrete Tomography;bregman Proximal Gradient Algorithms for Deep Matrix Factorization;Hessian Initialization Strategies for -BFGS Solving Non-linear Inverse Problems;inverse scale space Iterations for Non-convex variational Problems Using Functional Lifting;quantisation scale-spaces;A scaled and Adaptive FISTA Algorithm for Signal-Dependent Sparse Image Super-Resolution Problems;Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI;wasserstein Generative Models for Patch-Based Texture Synthesis;sketched Learning for Image Denoising;Translating Numerical Concepts for PDEs into Neural Architectures;CLIP: Cheap Lipschitz Training of Neural Networks;variational Models for Signal Processing with Graph Neural Networks;synthetic Images as a Regularity Prior for Image Restoration Neural Networks;geometric Deformation on Objects: Unsupervised Image Manipulation via Conjugation;learning Local Regularization for variational Image Restoration;Equivariant Deep Learning via Morphological and Linear scale space PDEs on the space of Positions and Orientations;on the Correspondence Between Replicator Dynamics and Assignment Flows;learning Linear Assignment Flows for Image Labeling via Exponential Integration;on the Geometric Mechanics of Assignment Flows for Metric Data Labeling;a Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration.

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Feature-based image analysis

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international JOURNAL OF computer vision 2003年第2-3期52卷 73-95页

作者： Lillholm, M Nielsen, M Griffin, LD IT Univ Copenhagen Copenhagen NV Denmark Kings Coll London London WC2R 2LS England

According to Marr's paradigm of computational vision the first process is an extraction of relevant features. the goal of this paper is to quantify and characterize the information carried by features using image-structure measured at feature-points to reconstruct images. In this way, we indirectly evaluate the concept of feature-based image analysis. the main conclusions are that (i) a reasonably low number of features characterize the image to such a high degree, that visually appealing reconstructions are possible, (ii) different feature-types complement each other and all carry important information. the strategy is to define metamery classes of images and examine the information content of a canonical least informative representative of this class. Algorithms for identifying these are given. Finally, feature detectors localizing the most informative points relative to different complexity measures derived from models of natural image statistics, are given.

关键词： features stochastic complexity statistics of natural images least commitment scale-space image reconstruction variational methods

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scale space and variational methods in computer vision 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Abderrahim Elmoataz Julien Rabin Jalal Fadili Loïc Simon Yvain Quéau

ISBN: (数字)9783030755492

ISBN: (纸本)9783030755485

this book constitutes the proceedings of the 8th international conference on scale space and variational methods in computer vision, ssvm 2021, which took place during May 16-20, 2021. the conference was planned to take place in Cabourg, France, but changed to an online format due to the COVID-19 pandemic.;the 45 papers included in this volume were carefully reviewed and selected from a total of 64 submissions. they were organized in topical sections named as follows: scale space and partial differential equations methods; flow, motion and registration; optimization theory and methods in imaging; machine learning in imaging; segmentation and labelling; restoration, reconstruction and interpolation; and inverse problems in imaging.

关键词： Image Processing and computer vision computer Communication Networks computer Appl. in Social and Behavioral Sciences Machine Learning Mathematics of Computing Pattern Recognition

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

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

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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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Local Culprits of Shape Complexity 8th

Local Culprits of Shape Complexity

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

作者： Arslan, Mazlum Ferhat Tari, Sibel Department of Computer Engineering Middle East Technical University Ankara06800 Turkey

ISBN: (纸本)9783030755485

Quantifying shape complexity is useful in several practical problems in addition to being interesting from a theoretical point of view. In this paper, instead of assigning a single global measure of complexity, we propose a distributed coding where to each point on the shape domain a measure of its contribution to complexity is assigned. We define the shape simplicity as the expressibility of the shape via a prototype shape. To keep discussions concrete we focus on a case where the prototype is a rectangle. Nevertheless, the constructions in the paper is valid in higher dimensions where the prototype is a hyper-cuboid. thanks to the connection between differential operators and mathematical morphology, the proposed construction naturally extends to the case where diamonds serve as the prototypes. © 2021, Springer Nature Switzerland AG.

关键词： Mathematical morphology

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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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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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Learning Local Regularization for variational Image Restoration 8th

Learning Local Regularization for Variational Image Restora...

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

作者： Prost, Jean Houdard, Antoine Almansa, Andrés Papadakis, Nicolas Univ. Bordeaux Bordeaux INP CNRS IMB UMR 5251 Talence33400 France Université de Paris MAP5 CNRS Paris75006 France

ISBN: (纸本)9783030755485

In this work, we propose a framework to learn a local regularization model for solving general image restoration problems. this regularizer is defined with a fully convolutional neural network that sees the image through a receptive field corresponding to small image patches. the regularizer is then learned as a critic between unpaired distributions of clean and degraded patches using a Wasserstein generative adversarial networks based energy. this yields a regularization function that can be incorporated in any image restoration problem. the efficiency of the framework is finally shown on denoising and deblurring applications. © 2021, Springer Nature Switzerland AG.

关键词： Image reconstruction

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