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检索条件"任意字段=7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019"

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

作者： Dahl, Vedrana Andersen Dahl, Anders Bjorholm Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark

ISBN: (纸本)9783030223687;9783030223670

Faithful representation of pairwise pixel affinities is crucial for the outcome of spectral segmentation methods. In conventional affinity models only close-range pixels interact, and a variety of subsequent techniques aims at faster propagation of local grouping cues across long-range connections. In this paper we propose a general framework for constructing a full-range affinity matrix. Our affinity matrix consists of a global similarity matrix and an additive proximity matrix. the similarity in appearance, including intensity and texture, is encoded for each pair of image pixels. Despite being full-range, our similarity matrix has a simple decomposition, which exploits an assignment of image pixels to dictionary elements. the additive proximity enforces smoothness to the segmentation by imposing interactions between near-by pixels. Our approach allows us to assess the advantages of using a full-range affinity for various spectral segmentation problems. Within our general framework we develop a few variants of full affinity for experimental validation. the performance we accomplish on composite textured images is excellent, and the results on natural images are promising.

关键词： Image segmentation Spectral methods Affinity matrix

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Pseudodifferential Inpainting: the Missing Link Between PDE- and RBF-Based Interpolation 7th

Pseudodifferential Inpainting: The Missing Link Between PDE-...

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

作者： Augustin, Matthias Weickert, Joachim Andris, Sarah Saarland Univ Math Image Anal Grp Fac Math & Comp Sci Campus E1-7 D-66041 Saarbrucken Germany

ISBN: (纸本)9783030223687;9783030223670

Inpainting with partial differential equations (PDEs) has been used successfully to reconstruct missing parts of an image, even for sparse data. On the other hand, sparse data interpolation is a rich field of its own with different methods such as scattered data interpolation with radial basis functions (RBFs). the goal of this paper is to establish connections between inpainting with linear shift- and rotation-invariant differential operators and interpolation with radial basis functions. the bridge between these two worlds is built by generalising inpainting methods to handle pseudo-differential operators and by considering their Green's functions. In this way, we find novel relations of various multiquadrics to pseudodifferential operators. Moreover, we show that many popular radial basis functions are related to processes from the diffusion and scale-space literature. We present a single numerical algorithm for all methods. It combines conjugate gradients with pseudodifferential operator evaluations in the Fourier domain. Our experiments show that the linear PDE- and the RBF-based communities have developed concepts of comparable quality.

关键词： Inpainting Sparse data Partial differential equations Pseudodifferential operators Scattered data interpolation Radial basis functions Green's functions

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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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Time Discrete Geodesics in Deep Feature spaces for Image Morphing 7th

Time Discrete Geodesics in Deep Feature Spaces for Image Mor...

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

作者： Effland, Alexander Kobler, Erich Pock, thomas Rumpf, Martin Graz Univ Technol Inst Comp Graph & Vis Graz Austria Univ Bonn Inst Numer Simulat Bonn Germany

ISBN: (纸本)9783030223687;9783030223670

Image morphing in computer vision amounts to computing a visually appealing transition of two images. A prominent model for image morphing originally proposed by Trouve, Younes and coworkers is image metamorphosis. Here, the space of images is endowed with a Riemannian metric that separately quantifies the contributions due to transport and image intensity variations along a transport path. Geodesic curves in this Riemannian space of images give rise to morphing transitions. the classical metamorphosis model considers images as square-integrable functions on some image domain and thus is non-sensitive to image features such as sharp interfaces or fine texture patterns. To resolve this drawback, we treat images not as intensity maps, but rather as maps into some feature space that encode local structure information. In the simplest case, color intensities are such feature vectors. To appropriately treat local structures and semantic information, deep convolutional neural network features are investigated. the resulting model is formulated directly in terms of a variational time discretization developed for the classical metamorphosis model by Berkels, Effland and Rumpf. the key ingredient is a mismatch energy that locally approximates the squared Riemannian distance and consists of a regularization energy of the time discrete flow and a dissimilarity energy that measures the feature vector modulation along discrete transport paths. the spatial discretization is based on a finite difference and a stable spline interpolation. A variety of numerical examples demonstrates the robustness and versatility of the proposed method for real images using a variant of the iPALM algorithm for the minimization of the fully discrete energy functional.

关键词： Image metamorphosis Image morphing Image registration Feature space Convolutional neural network iPALM algorithm

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Total Variation and Mean Curvature PDEs on the space of Positions and Orientations 7th

Total Variation and Mean Curvature PDEs on the Space of Posi...

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

作者： Duits, Remco St-Onge, Etienne Portegies, Jim Smets, Bart Eindhoven Univ Technol CASA Eindhoven Netherlands Sherbrooke Connect Imaging Lab SCIL Sherbrooke PQ Canada

ISBN: (纸本)9783030223687;9783030223670

Total variation regularization and total variation flows (TVF) have been widely applied for image enhancement and denoising. To include a generic preservation of crossing curvilinear structures in TVF we lift images to the homogeneous space M = R-d x Sd-1 of positions and orientations as a Lie group quotient in SE(d). For d = 2 this is called 'total roto-translation variation' by Chambolle & Pock. We extend this to d = 3, by a PDE-approach with a limiting procedure for which we prove convergence. We also include a Mean Curvature Flow (MCF) in our PDE model on M. this was first proposed for d = 2 by Citti et al. and we extend this to d = 3. Furthermore, for d = 2 we take advantage of locally optimal differential frames in invertible orientation scores (OS). We apply our TVF and MCF in the denoising/enhancement of crossing fiber bundles in DW-MRI. In comparison to data-driven diffusions, we see a better preservation of bundle boundaries and angular sharpness in fiber orientation densities at crossings. We support this by error comparisons on a noisy DW-MRI phantom. We also apply our TVF and MCF in enhancement of crossing elongated structures in 2D images via OS, and compare the results to nonlinear diffusions (CED-OS) via OS.

关键词： Total variation Mean Curvature Sub-Riemannian Geometry Roto-translations Denoising Fiber enhancement

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PDE Evolutions for M-Smoothers: From Common Myths to Robust Numerics 7th

PDE Evolutions for M-Smoothers: From Common Myths to Robust ...

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

作者： Welk, Martin Weickert, Joachim Private Univ Hlth Sci Med Informat & Technol Inst Biomed Image Anal Eduard Wallnofer Zentrum 1 A-6060 Hall Tyrol Austria Saarland Univ Math Image Anal Grp Campus E1-7 D-66041 Saarbrucken Germany

ISBN: (纸本)9783030223687;9783030223670

Local M-smoothers constitute an interesting and important class of image processing techniques with many connections to other methods. In our paper we derive a family of partial differential equations (PDEs) that result as limiting processes from M-smoothers which are based on local order-p means within a disc the radius of which tends to zero. the order p may take any nonzero value > -1. thus, we also allow negative values which have never been considered before. In contrast to results from the literature, we show in the space-continuous case that mode filtering does not arise for p -> 0, but for p -> -1. Extending our filter class to p-values smaller than - 1 allows to include e.g. the classical image sharpening flow of Gabor. Since our PDE class is highly anisotropic and may contain backward parabolic operators, designing adequate numerical methods is difficult. We present an L-infinity-stable explicit finite difference scheme that satisfies a discrete maximum-minimum principle, is fairly efficient, and offers excellent rotation invariance. Although it solves parabolic PDEs, it makes consequent use of stabilisation concepts from the numerics of hyperbolic PDEs. Our experiments show that the PDEs forp < 1 are of specific interest: their backward parabolic term creates favourable sharpening properties, while they appear to maintain the strong shape simplification properties of mean curvature motion.

关键词： M-smoother Partial differential equation Mode filter Backward parabolic operator Anisotropy Finite difference methods Shape analysis

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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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Refitting Solutions Promoted by 12 Sparse Analysis Regularizations with Block Penalties

Refitting Solutions Promoted by 12 Sparse Analysis Regulariz...

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

作者： Deledalle, Charles-Alban Papadakis, Nicolas Salmon, Joseph Vaiter, Samuel Department of Electrical and Computer Engineering University of California San Diego La Jolla United States CNRS Univ. Bordeaux IMB UMR 5251 Talence33400 France IMAG Univ. Montpellier CNRS Montpellier France IMB CNRS Université de Bourgogne Dijon21078 France

ISBN: (纸本)9783030223670

In inverse problems, the use of an L12 analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact while keeping information of interest contained in the biased solution. this is done through the use of refitting block penalties that only act on the co-support of the estimation. Based on an analysis of related works in the literature, we propose a new penalty that is well suited for refitting purposes. We also present an efficient algorithmic method to obtain the refitted solution along with the original (biased) solution for any convex refitting block penalty. Experiments illustrate the good behavior of the proposed block penalty for refitting. © 2019, Springer Nature Switzerland AG.

关键词： Inverse problems

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Time Discrete Extrapolation in a Riemannian space of Images 6th

Time Discrete Extrapolation in a Riemannian Space of Images

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6th international conference on scale space and variational methods in computer vision (ssvm)

作者： Effland, Alexander Rumpf, Martin Schaefer, Florian Univ Bonn Inst Numer Simulat Bonn Germany CALTECH Pasadena CA 91125 USA

ISBN: (纸本)9783319587714;9783319587707

the Riemannian metamorphosis model introduced and analyzed in [7,12] is taken into account to develop an image extrapolation tool in the space of images. To this end, the variational time discretization for the geodesic interpolation proposed in [2] is picked up to define a discrete exponential map. For a given weakly differentiable initial image and a sufficiently small initial image variation it is shown how to compute a discrete geodesic extrapolation path in the space of images. the resulting discrete paths are indeed local minimizers of the corresponding discrete path energy. A spatial Galerkin discretization with cubic splines on coarse meshes for image deformations and piecewise bilinear finite elements on fine meshes for image intensity functions is used to derive a fully practical algorithm. the method is applied to real images and image variations recorded with a digital camera.

关键词： Image extrapolation Shape space Elastic registration Exponential map

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Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition 6th

Combining Contrast Invariant L1 Data Fidelities with Nonline...

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6th international conference on scale space and variational methods in computer vision (ssvm)

作者： Zeune, Leonie van Gils, Stephan A. Terstappen, Leon W. M. M. Brune, Christoph Univ Twente Dept Appl Math NL-7522 NB Enschede Netherlands Univ Twente Dept Med Cell BioPhys NL-7522 NB Enschede Netherlands

ISBN: (纸本)9783319587714;9783319587707

this paper focuses on multi-scale approaches for variational methods and corresponding gradient flows. Recently, for convex regularization functionals such as total variation, new theory and algorithms for nonlinear eigenvalue problems via nonlinear spectral decompositions have been developed. those methods open new directions for advanced image filtering. However, for an effective use in image segmentation and shape decomposition, a clear interpretation of the spectral response regarding size and intensity scales is needed but lacking in current approaches. In this context, L-1 data fidelities are particularly helpful due to their interesting multi-scale properties such as contrast invariance. Hence, the novelty of this work is the combination of L-1-based multi-scale methods with nonlinear spectral decompositions. We compare L-1 with L-2 scale-space methods in view of spectral image representation and decomposition. We show that the contrast invariant multi-scale behavior of L-1-TV promotes sparsity in the spectral response providing more informative decompositions. We provide a numerical method and analyze synthetic and biomedical images at which decomposition leads to improved segmentation.

关键词： L-1-TV Denoising scale-spaces Nonlinear spectral decomposition Multiscale segmentation Eigenfunction Calibrable sets

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