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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21-30 订阅

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Learning Posterior Distributions in Underdetermined Inverse Problems 9th

Learning Posterior Distributions in Underdetermined Inverse...

引用

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

Fluctuation-Based Deconvolution in Fluorescence Microscopy ...

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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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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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scale space and variational methods in computer vision: 5th international conference, ssvm 2015 Lège-Cap Ferret, France, May 31 - June 4, 2015 proceedings 5

Scale space and variational methods in computer vision: 5th ...

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

作者： Aujol, Jean-François Nikolova, Mila Papadakis, Nicolas University of Bordeaux Talence France CNRS ENS Cachan Cachan France CNRS University of Bordeaux Talence France

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the Fractional Harris-Laplace Feature Detector 7th

The Fractional Harris-Laplace Feature Detector

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7th international conference on scale space and variational methods in computer vision (ssvm)

作者： Adams, Matthew Univ Calgary 2500 Univ Dr NW Calgary AB T2N 1N4 Canada

ISBN: (纸本)9783030223687;9783030223670

Fractional calculus is an extension of integer-order differentiation and integration which explains many natural physical processes. New applications of the fractional calculus are in constant development. the current paper introduces fractional differentiation to feature detection in digital images. the Harris-Laplace feature detector is adapted to use the non-local properties of the fractional derivative to include more information about image pixel perturbations when quantifying features. Using fractional derivatives in the Harris-Laplace detector leads to higher repeatability when detecting features in grayscale images. Applications of this development are suggested.

关键词： Feature detection Image processing Fractional calculus

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Direct MRI Segmentation from k-space Data by Iterative Potts Minimization 7th

Direct MRI Segmentation from k-Space Data by Iterative Potts...

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7th international conference on scale space and variational methods in computer vision (ssvm)

作者： Kiefer, Lukas Petra, Stefania Storath, Martin Weinmann, Andreas Heidelberg Univ Math Imaging Grp Heidelberg Germany Hsch Darmstadt Dept Math & Nat Sci Darmstadt Germany Hsch Wurzburg Schweinfurt Dept Appl Nat Sci & Humanities Wurzburg Germany Helmholtz Zentrum Munchen Inst Computat Biol Munich Germany

ISBN: (纸本)9783030223687;9783030223670

We consider the problem of image segmentation in magnetic resonance imaging (MRI) directly from k-space measurements as opposed to more standard approaches that first reconstruct the image before segmenting it. the model we employ is the piecewise-constant Mumford-Shah model (the Potts model) in connection with the MRI reconstruction problem. the output of our proposed scheme is a piecewise-constant function which yields a segmentation of the image domain. To solve the involved non-convex and non-smooth optimization problem, we adopt an iterative minimization strategy based on surrogate functionals. Numerical experiments illustrate the potential of the approach when applied on undersampled MRI data.

关键词： MRI segmentation Joint reconstruction and segmentation Potts model Piecewise-constant Mumford-Shah model Regularization of inverse problems

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A Cortical-Inspired Model for Orientation-Dependent Contrast Perception: A Link with Wilson-Cowan Equations 7th

A Cortical-Inspired Model for Orientation-Dependent Contrast...

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7th international conference on scale space and variational methods in computer vision (ssvm)

作者： Bertalmio, Marcelo Calatroni, Luca Franceschi, Valentina Franceschiello, Benedetta Prandi, Dario Univ Pompeu Fabra DTIC Barcelona Spain Ecole Polytech CNRS CMAP Palaiseau France Univ Paris Sud IMO Orsay France Fdn Asile Aveugles Lausanne Switzerland Lab Invest Neurophysiol Lausanne Switzerland Cent Supelec CNRS L2S Gif Sur Yvette France

ISBN: (纸本)9783030223687;9783030223670

We consider a differential model describing neuro-physiological contrast perception phenomena induced by surrounding orientations. the mathematical formulation relies on a cortical-inspired modelling [11] largely used over the last years to describe neuron interactions in the primary visual cortex (V1) and applied to several image processing problems [14,15,21]. Our model connects to Wilson-Cowan-type equations [26] and it is analogous to the one used in [3,4,16] to describe assimilation and contrast phenomena, the main novelty being its explicit dependence on local image orientation. To confirm the validity of the model, we report some numerical tests showing its ability to explain orientation-dependent phenomena (such as grating induction) and geometric-optical illusions [18,24] classically explained only by filtering-based techniques [7,20].

关键词： Orientation-dependent modelling Wilson-Cowan equations Primary visual cortex Contrast perception variational modelling

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scale space and variational methods in computer vision - 4th international conference, ssvm 2013, Proceedings

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4th international conference on scale space and variational methods in computer vision, ssvm 2013

ISBN: (纸本)9783642382666

the proceedings contain 42 papers. the topics discussed include: targeted iterative filtering;generalized gradient on vector bundle application to image denoising;expert regularizers for task specific processing;a spectral approach to total variation;convex generalizations of total variation based on the structure tensor with applications to inverse problems;adaptive second-order total variation: an approach aware of slope discontinuities;variational methods for motion deblurring with still background;blind deblurring using a simplified sharpness index;a cascadic alternating Krylov subspace image restoration method;B-SMART: Bregman-based first-order algorithms for non-negative compressed sensing problems;and epigraphical projection for solving least squares anscombe transformed constrained optimization problems.

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variational Registration of Multiple Images with the SVD Based $$\mathrm {S}q\mathrm {N}$$ Distance Measure

Variational Registration of Multiple Images with the SVD Bas...

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7th international conference on scale space and variational methods in computer vision, ssvm 2019

作者： Brehmer, Kai Aggrawal, Hari Om Heldmann, Stefan Modersitzki, Jan Institute of Mathematics and Image Computing University of Lübeck Lübeck Germany Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany

ISBN: (纸本)9783030223670

Image registration, especially the quantification of image similarity, is an important task in image processing. Various approaches for the comparison of two images are discussed in the literature. However, although most of these approaches perform very well in a two image scenario, an extension to a multiple images scenario deserves attention. In this article, we discuss and compare registration methods for multiple images. Our key assumption is, that information about the singular values of a feature matrix of images can be used for alignment. We introduce, discuss and relate three recent approaches from the literature: the Schatten q-norm based (Formula Presented) distance measure, a rank based approach, and a feature volume based approach. We also present results for typical applications such as dynamic image sequences or stacks of histological sections. Our results indicate that the (Formula Presented) approach is in fact a suitable distance measure for image registration. Moreover, our examples also indicate that the results obtained by (Formula Presented) are superior to those obtained by its competitors. © 2019, Springer Nature Switzerland AG.

关键词： Image registration

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