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检索条件"任意字段=1st International Conference on Scale Space and Variational Methods in Computer Vision"

共 139 条记录，以下是71-80 订阅

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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 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Xue-Cheng Tai Knut Mørken Marius Lysaker Knut-Andreas Lie

ISBN: (数字)9783642022562

ISBN: (纸本)9783642022555

This book contains 71 original, scienti?c articles that address state-of-the-art researchrelatedto scale space and variationalmethods for image processing and computer vision. Topics covered in the book range from mathematical analysis of both established and new models, fast numerical methods, image analysis, segmentation, registration, surface and shape construction and processing, to real applications in medical imaging and computer vision. The ideas of scale spaceandvariationalmethodsrelatedtopartialdi?erentialequationsarecentral concepts. The papers re?ect the newest developments in these ?elds and also point to the latest literature. All the papers were submitted to the Second international conference on scale space and variational methods in computer vision, which took place in Voss, Norway, during June 1–5, 2009. The papers underwent a peer review process similar to that of high-level journals in the ?eld. We thank the authors, the Scienti?c Committee, the Program Committee and the reviewers for their hard work and helpful collaboration. Their contribution has been crucial for the e?cient processing of this book, and for the success of the conference.

关键词： Image Processing and computer vision Programming Techniques Data Mining and Knowledge Discovery computer Graphics computer Imaging, vision, Pattern Recognition and Graphics Pattern Recognition

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EmNeF: Neural Fields for Embedded variational Problems in Imaging 9th

EmNeF: Neural Fields for Embedded Variational Problems in ...

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

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

ISBN: (纸本)9783031319747

We propose a model-driven neural fields approach for solving variational problems. The approach can be applied to a variety of problems with convex, 1-homogeneous regularizer and arbitrary, possibly non-convex, data term. Our strategy is to embed the non-convex energy into a higher-dimensional space, reaching a convex primal-dual formulation. Instead of using classical gradient-descent based optimization algorithms, we propose training multiple fields representing the primal and dual variables in order to solve the problem. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Gradient methods

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The Convex-Hull-stripping Median Approximates Affine Curvature Motion 7th

The Convex-Hull-Stripping Median Approximates Affine Curvatu...

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

作者： Welk, Martin Breuss, Michael Private Univ Hlth Sci Med Informat & Technol Inst Biomed Image Anal Eduard Wallnofer Zentrum 1 A-6060 Hall Tyrol Austria Brandenburg Univ Technol Cottbus Senftenberg Pl Deutsch Einheit 1 D-03046 Cottbus Germany

ISBN: (纸本)9783030223687;9783030223670

The median filter is one of the fundamental filters in image processing. Its standard realisation relies on a rank ordering of given data which is easy to perform if the given data are scalar values. However, the generalisation of the median filter to multivariate data is a delicate issue. One of the methods of potential interest for computing a multivariate median is the convex-hull-stripping median from the statistics literature. Its definition is of purely algorithmical nature, and it offers the advantageous property of affine equivariance. While it is a classic result that the standard median filter approximates mean curvature motion, no corresponding assertion has been established up to now for the convex-hull-stripping median. The aim of our paper is to close this gap in the literature. In order to provide a theoretical foundation for the convex-hull-stripping median of multivariate images, we investigate its continuous-scale limit. It turns out that the resulting evolution is described by the well-known partial differential equation of affine curvature motion. Thus we have established in this paper a relation between two important models from image processing and statistics. We also present some experiments that support our theoretical findings.

关键词： Median filter Convex hull stripping Partial differential equations Curve evolution

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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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variational dense motion estimation using the Helmholtz Decomposition

Variational dense motion estimation using the Helmholtz Deco...

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4th international conference on scale space methods in computer vision

作者： Kohlberger, T Mémin, É Schnörr, C Univ Mannheim Dept Math & Comp Sci Comp Vis Graph & Pattern Recognit Grp D-68131 Mannheim Germany Univ Rennes 1 IRISA F-35042 Rennes France

ISBN: (纸本)354040368X

We present a novel variational approach to dense motion estimation of highly non-rigid structures in image sequences. Our representation of the motion vector field is based on the extended Helmholtz Decomposition into its principal constituents: The laminar flow and two potential functions related to the solenoidal and irrotational flow, respectively. The potential functions, which are of primary interest for flow pattern analysis in numerous application fields like remote sensing or fluid mechanics, are directly estimated from image sequences with a variational approach. We use regularizers with derivatives up to third order to obtain unbiased high-quality solutions. Computationally, the approach is made tractable by means of auxiliary variables. The performance of the approach is demonstrated with ground-truth experiments and real-world data.

关键词： Laminar flow

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Shape Matching by Time Integration of Partial Differential Equations 6th

Shape Matching by Time Integration of Partial Differential E...

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

作者： Dachsel, Robert Breuss, Michael Hoeltgen, Laurent Brandenburg Tech Univ Cottbus Inst Math Pl Deutsch Einheit 1 D-03046 Cottbus Germany

ISBN: (纸本)9783319587714;9783319587707

The main task in three dimensional shape matching is to retrieve correspondences between two similar three dimensional objects. To this end, a suitable point descriptor which is invariant under isometric transformations is required. A commonly used descriptor class relies on the spectral decomposition of the Laplace-Beltrami operator. Important examples are the heat kernel signature and the more recent wave kernel signature. In previous works, the evaluation of the descriptor is based on eigenfunction expansions. Thereby a significant practical aspect is that computing a complete expansion is very time and memory consuming. Thus additional strategies are usually introduced that enable to employ only part of the full expansion. In this paper we explore an alternative solution strategy. We discretise the underlying partial differential equations (PDEs) not only in space as in the mentioned approaches, but we also tackle temporal parts by using time integration methods. Thus we do not perform eigenfunction expansions and avoid the use of additional strategies and corresponding parameters. We study here the PDEs behind the heat and wave kernel signature, respectively. Our shape matching experiments show that our approach may lead to quality improvements for finding correct correspondences in comparison to the eigenfunction expansion methods.

关键词： Shape matching Point descriptor Shape analysis Heat equation Schrodinger equation

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Denoising by Inpainting 6th

Denoising by Inpainting

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

作者： Adam, Robin Dirk Peter, Pascal Weickert, Joachim Saarland Univ Math Image Anal Grp Fac Math & Comp Sci Campus E1-7 D-66041 Saarbrucken Germany

ISBN: (纸本)9783319587714;9783319587707

The filling-in effect of diffusion processes has been successfully used in many image analysis applications. Examples include image reconstructions in inpainting-based compression or dense optic flow computations. As an interesting side effect of diffusion-based inpainting, the interpolated data are smooth, even if the known image data are noisy: Inpainting averages information from noisy sources. Since this effect has not been investigated for denoising purposes so far, we propose a general framework for denoising by inpainting. It averages multiple inpainting results from different selections of known data. We evaluate two concrete implementations of this framework: The first one specifies known data on a shifted regular grid, while the second one employs probabilistic densification to optimise the known pixel locations w.r.t. the inpainting quality. For homogeneous diffusion inpainting, we demonstrate that our regular grid method approximates the quality of its corresponding diffusion filter. The densification algorithm with homogeneous diffusion inpainting, however, shows edge-preserving behaviour. It resembles space-variant diffusion and offers better reconstructions than homogeneous diffusion filters.

关键词： Diffusion Denoising Inpainting Densification

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L₀-Norm and Total Variation for Wavelet Inpainting

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2nd international conference on scale space and variational methods in computer vision

作者： Yau, Andy C. Tai, Xue-Cheng Ng, Michael K. Nanyang Technol Univ Div Math Sci Sch Phys & Math Sci Singapore Singapore Univ Bergen Math Inst N-5020 Bergen Norway Hong Kong Baptist Univ Dept Math Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783642022555

In this paper, we suggest an algorithm to recover an image whose wavelet coefficients are partially lost. We propose a wavelet inpainting model by using L-0-norm and the total variation (TV) minimization. Traditionally, L-0-norm is replaced by L-1-norm or L-2-norm due to numerical difficulties. We use an alternating minimization technique to overcome these difficulties. In order to improve the numerical efficiency, we also apply a graph cut algorithm to Solve the subproblem related to TV minimization. Numerical results will be given to demonstrate our advantages of the proposed algorithm.

关键词： Graphic methods

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A multigrid platform for real-time motion computation with discontinuity-preserving variational methods

A multigrid platform for real-time motion computation with d...

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5th international conference on scale-space and PDE methods in computer vision

作者： Bruhn, Andres Weickert, Joachim Kohlberger, Timo Schnoerr, Christoph Univ Saarland Math Image Anal Grp Fac Math & Comp Sci D-66041 Saarbrucken Germany Univ Mannheim Dept Math & Comp Sci Comp Vis Graph & Pattern Recognit Grp D-68131 Mannheim Germany

variational methods are among the most accurate techniques for estimating the optic flow. They yield dense flow fields and can be designed such that they preserve discontinuities, estimate large displacements correctly and perform well under noise and varying illumination. However, such adaptations render the minimisation of the underlying energy functional very expensive in terms of computational costs: Typically one or more large linear or nonlinear equation systems have to be solved in order to obtain the desired solution. Consequently, variational methods are considered to be too slow for real-time performance. In our paper we address this problem in two ways: (i) We present a numerical framework based on bidirectional multigrid methods for accelerating a broad class of variational optic flow methods with different constancy and smoothness assumptions. Thereby, our work focuses particularly on regularisation strategies that preserve discontinuities. (ii) We show by the examples of five classical and two recent variational techniques that real-time performance is possible in all cases-even for very complex optic flow models that offer high accuracy. Experiments show that frame rates up to 63 dense flow fields per second for image sequences of size 160 x 120 can be achieved on a standard PC. Compared to classical iterative methods this constitutes a speedup of two to four orders of magnitude.

关键词： partial differential equations variational methods optic flow multigrid methods

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