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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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Optical flow on evolving surfaces with an application to the analysis of 4D microscopy data

引用

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

作者： Kirisits, Clemens Lang, Lukas F. Scherzer, Otmar Computational Science Center University of Vienna Nordbergstr. 15 1090 Vienna Austria Radon Institute of Computational and Applied Mathematics Austrian Academy of Sciences Altenberger Str. 69 4040 Linz Austria

ISBN: (纸本)9783642382666

We extend the concept of optical flow to a dynamic non-Euclidean setting. Optical flow is traditionally computed from a sequence of flat images. It is the purpose of this paper to introduce variational motion estimation for images that are defined on an evolving surface. Volumetric microscopy images depicting a live zebrafish embryo serve as both biological motivation and test data. © 2013 Springer-Verlag.

关键词： Optical flows

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Blind space-Variant Single-Image Restoration of Defocus Blur 6th

Blind Space-Variant Single-Image Restoration of Defocus Blur

引用

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

作者： Bar, Leah Sochen, Nir Kiryati, Nahum Tel Aviv Univ Dept Appl Math IL-69978 Tel Aviv Israel Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel

ISBN: (纸本)9783319587714;9783319587707

We address the problem of blind piecewise space-variant image deblurring where only part of the image is sharp, assuming a shallow depth of field which imposes significant defocus blur. We propose an automatic image recovery approach which segments the sharp and blurred sub-regions, iteratively estimates a non-parametric blur kernel and restores the sharp image via a variational non-blind space variant method. We present a simple and efficient blur measure which emphasizes the blur difference of the sub-regions followed by a blur segmentation procedure based on an evolving level set function. One of the contributions of this work is the extension to the space-variant case of progressive blind deconvolution recently proposed, an iterative process consisting of non-parametric blind kernel estimation and residual blur deblurring. Apparently this progressive strategy is superior to the one step deconvolution procedure. Experimental results on real images demonstrate the effectiveness of the proposed algorithm.

关键词： space-variant deblurring Blind deconvolution Blur segmentation

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Vehicle X-ray Scans Registration: A One-Dimensional Optimization Problem 6th

Vehicle X-ray Scans Registration: A One-Dimensional Optimiza...

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

作者： Marciano, Abraham Cohen, Laurent D. Gadi, Najib Univ Paris 09 PSL Res Univ CNRS CEREMADEUMR 7534 F-75016 Paris France Smiths Detect F-94405 Vitry Sur Seine France

ISBN: (纸本)9783319587714;9783319587707

Over the years, image registration has been largely employed in medical applications, robotics and geophysics. More recently, it has increasingly drawn attention of security and defense industries, particularly aiming at threat detection automation. this paper first introduces a short overview of mathematical methods for image registration, with a focus on variational approaches. In a second part, a specific registration task is presented: the optimal alignment between X-ray scans of an inspected vehicle and an empty reference of the same car model. Indeed, while being scanned by dedicated imaging systems, the car speed is not necessarily constant which may entail non-rigid deformations in the resulting image. the paper simply addresses this issue by applying a rigid transform on the reference image before using the variational framework solved in one dimension. For convergence and speed purposes, line-search techniques and a multiscale approach are used.

关键词： Image registration variational approach One-dimensional optimization

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Evaluating Data Terms for variational Multi-frame Super-Resolution 6th

Evaluating Data Terms for Variational Multi-frame Super-Reso...

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

作者： Bodduna, Kireeti Weickert, Joachim Saarland Univ Math Image Anal Grp Fac Math & Comp Sci D-66041 Saarbrucken Germany

ISBN: (纸本)9783319587714;9783319587707

We present the first systematic evaluation of the data terms for multi-frame super-resolution within a variational model. the various data terms are derived by permuting the order of the blur-, downsample-, and warp-operators in the image acquisition model. this yields six different basic models. Our experiments using synthetic images with known ground truth show that two models are preferable: the widely-used warp-blur-downsample model that is physically plausible if atmospheric blur is negligible, and the hardly considered blur-warp-downsample model. We show that the quality of motion estimation plays the decisive role on which of these two models works best: While the classic warp-blur-downsample model requires optimal motion estimation, the rarely-used blur-warp-downsample model should be preferred in practically relevant scenarios when motion estimation is suboptimal. this confirms a widely ignored result by Wang and Qi (2004). Last but not least, we propose a new modification of the blur-warp-downsample model that offers a very significant speed-up without substantial loss in the reconstruction quality.

关键词： Super-resolution variational methods Inverse problems

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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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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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Multi scale shape index for 3D object recognition

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

作者： Bonde, Ujwal Badrinarayanan, Vijay Cipolla, Roberto Deparment of Engineering University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9783642382666

We present Multi scale Shape Index (MSSI), a novel feature for 3D object recognition. Inspired by the scale space filtering theory and Shape Index measure proposed by Koenderink & Van Doorn [6], this feature associates different forms of shape, such as umbilics, saddle regions, parabolic regions to a real valued index. this association is useful for representing an object based on its constituent shape forms. We derive closed form scale space equations which computes a characteristic scale at each 3D point in a point cloud without an explicit mesh structure. this characteristic scale is then used to estimate the Shape Index. We quantitatively evaluate the robustness and repeatability of the MSSI feature for varying object scales and changing point cloud density. We also quantify the performance of MSSI for object category recognition on a publicly available dataset. © 2013 Springer-Verlag.

关键词： Object recognition

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Order-Adaptive Regularisation for variational Optical Flow: Global, Local and in Between 6th

Order-Adaptive Regularisation for Variational Optical Flow: ...

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

作者： Maurer, Daniel Stoll, Michael Bruhn, Andres Univ Stuttgart Comp Vis & Intelligent Syst Grp Inst Visualizat & Interact Syst D-70569 Stuttgart Germany

ISBN: (纸本)9783319587714;9783319587707

Recent approaches for variational motion estimation typically either rely on first or second order regularisation strategies. While first order strategies are more appropriate for scenes with fronto-parallel motion, second order constraints are superior if it comes to the estimation of affine flow fields. Since using the wrong regularisation order may lead to a significant deterioration of the results, it is surprising that there has not been much effort in the literature so far to determine this order automatically. In our work, we address the aforementioned problem in two ways. (i) First, we discuss two anisotropic smoothness terms of first and second order, respectively, that share important structural properties and that are thus particularly suited for being combined within an order-adaptive variational framework. (ii) Secondly, based on these two smoothness terms, we develop four different variational methods and with it four different strategies for adaptively selecting the regularisation order: a global and a local strategy based on half-quadratic regularisation, a non-local approach that relies on neighbourhood information, and a region based method using level sets. Experiments on recent benchmarks show the benefits of each of the strategies. Moreover, they demonstrate that adaptively combining different regularisation orders not only allows to outperform single-order strategies but also to obtain advantages beyond the ones of a frame-wise selection.

关键词： Higher order regularisation variational methods Optical flow

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A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow 6th

A Comparison of Isotropic and Anisotropic Second Order Regul...

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

作者： Maurer, Daniel Stoll, Michael Volz, Sebastian Gairing, Patrick Bruhn, Andres Univ Stuttgart Comp Vis & Intelligent Syst Grp Inst Visualizat & Interact Syst D-70569 Stuttgart Germany

ISBN: (纸本)9783319587714;9783319587707

In variational optical flow estimation, second order regularisation plays an important role, since it offers advantages in the context of non-fronto-parallel motion. However, in contrast to first order smoothness constraints, most second order regularisers are limited to isotropic concepts. Moreover, the few existing anisotropic concepts are lacking a comparison so far. Hence, our contribution is twofold. (i) First, we juxtapose general concepts for isotropic and anisotropic second order regularization based on direct second order methods, infimal convolution techniques, and indirect coupling models. For all the aforementioned strategies suitable optical flow regularisers are derived. (ii) Second, we show that modelling anisotropic second order smoothness terms gives an additional degree of freedom when penalising deviations from smoothness. this in turn allows us to propose a novel anisotropic strategy which we call double anisotropic regularisation. Experiments on the two KITTI benchmarks show the qualitative differences between the different strategies. Moreover, they demonstrate that the novel concept of double anisotropic regularisation is able to produce excellent results.

关键词： Higher order regularisation variational methods Optical flow

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Constrained sparse texture synthesis

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

作者： Tartavel, Guillaume Gousseau, Yann Peyré, Gabriel LTCI CNRS Telecom ParisTech France Ceremade Université Paris-Dauphine France

ISBN: (纸本)9783642382666

this paper presents a novel texture synthesis algorithm that performs a sparse expansion of the patches of the image in a dictionary learned from an input exemplar. the synthesized texture is computed through the minimization of a non-convex energy that takes into account several constraints. Our first contribution is the computation of a sparse expansion of the patches imposing that the dictionary atoms are used in the same proportions as in the exemplar. this is crucial to enable a fair representation of the features of the input image during the synthesis process. Our second contribution is the use of additional penalty terms in the variational formulation to maintain the histogram and the low frequency content of the input. Lastly we introduce a non-linear reconstruction process that stitches together patches without introducing blur. Numerical results illustrate the importance of each of these contributions to achieve state of the art texture synthesis. © 2013 Springer-Verlag.

关键词： computer vision

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