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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 81 条记录，以下是31-40 订阅

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Corner Detection Using the Affine Morphological scale space 6th

Corner Detection Using the Affine Morphological Scale Space

引用

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

作者： Alvarez, Luis Univ Las Palmas Gran Canaria Dept Informat & Sistemas Campus Tafira Las Palmas Gran Canaria 35017 Spain

ISBN: (纸本)9783319587714;9783319587707

We introduce a method for corner estimation based on the affine morphological scale space (AMSS). Using some explicit known formula about corner evolution across AMSS, proven by Alvarez and Morales in 1997, we define a morphological cornerness measure based on the expected evolution of an ideal corner across AMSS. We define a new procedure to track the corner motion across AMSS. To evaluate the accuracy of the method we study in details the results for a collection of synthetic corners with angles from 15 to 160 degrees. We also present experiments in real images and we show that the proposed method can also automatically handle the case of multiple junctions.

关键词： Affine scale space Corner detection Morphology

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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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A Unified Framework for the Restoration of Images Corrupted by Additive White Noise 6th

A Unified Framework for the Restoration of Images Corrupted ...

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

作者： Lanza, Alessandro Sciacchitano, Federica Morigi, Serena Sgallari, Fiorella Univ Bologna Dept Math Bologna Italy Univ Genoa Dept Math Genoa Italy

ISBN: (纸本)9783319587714;9783319587707

We propose a robust variational model for the restoration of images corrupted by blur and the general class of additive white noises. the solution of the non-trivial optimization problem, due to the non-smooth non-convex proposed model, is efficiently obtained by an Alternating Directions Method of Multipliers (ADMM), which in particular reduces the solution to a sequence of convex optimization sub-problems. Numerical results show the potentiality of the proposed model for restoring blurred images corrupted by several kinds of additive white noises.

关键词： variational image restoration Additive white noise Total Variation Non-convex non-smooth optimization ADMM

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Beyond Multi-view Stereo: Shading-Reflectance Decomposition 6th

Beyond Multi-view Stereo: Shading-Reflectance Decomposition

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

作者： Melou, Jean Queau, Yvain Durou, Jean-Denis Castan, Fabien Cremers, Daniel Univ Toulouse UMR CNRS 5505 IRIT Toulouse France Tech Univ Munich Dept Informat Munich Germany Mikros Image Levallois Perret France

ISBN: (纸本)9783319587714;9783319587707

We introduce a variational framework for separating shading and reflectance from a series of images acquired under different angles, when the geometry has already been estimated by multi-view stereo. Our formulation uses an l(1)-TV variational framework, where a robust photometric-based data term enforces adequation to the images, total variation ensures piecewise-smoothness of the reflectance, and an additional multi-view consistency term is introduced for resolving the arising ambiguities. Optimisation is carried out using an alternating optimisation strategy building upon iteratively reweighted least-squares. Preliminary results on both a synthetic dataset, using various lighting and reflectance scenarios, and a real dataset, confirm the potential of the proposed approach.

关键词： Reflectance Multi-view Shading variational methods

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An Optimal Transport-Based Restoration Method for Q-Ball Imaging 6th

An Optimal Transport-Based Restoration Method for Q-Ball Ima...

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

作者： Vogt, thomas Lellmann, Jan Univ Lubeck Inst Math & Image Comp MIC Lubeck Germany

ISBN: (纸本)9783319587714;9783319587707

We propose a variational approach for edge-preserving total variation (TV)-based regularization of Q-ball data from high angular resolution diffusion imaging (HARDI). While total variation is among the most popular regularizers for variational problems, its application to orientation distribution functions (ODF), as they naturally arise in Q-ball imaging, is not straightforward. We propose to use an extension that specifically takes into account the metric on the underlying orientation space. the key idea is to write the difference quotients in the TV seminorm in terms of the Wasserstein statistical distance from optimal transport. We combine this regularizer with a matchingWasserstein data fidelity term. Using the Kantorovich-Rubinstein duality, the variational model can be formulated as a convex optimization problem that can be solved using a primal-dual algorithm. We demonstrate the effectiveness of the proposed framework on real and synthetic Q-ball data.

关键词： variational methods Total variation Q-ball imaging Wasserstein distance

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Adaptive Discretizations for Non-smooth variational vision 6th

Adaptive Discretizations for Non-smooth Variational Vision

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

作者： Estellers, Virginia Soatto, Stefano Univ Calif Los Angeles Vis Lab Los Angeles CA 90095 USA

ISBN: (纸本)9783319587714;9783319587707

variational problems in vision are solved numerically on the pixel lattice because it provides the simplest computational grid to discretize the input images, even though a uniform grid seldom matches the complexity of the solution. To adapt the complexity of the discretization to the solution, it is necessary to adopt finite-element techniques that match the resolution of piecewise polynomial bases to the resolving power of the variational model, but such techniques have been overlooked for nonsmooth variational models. To address this issue, we investigate the pros and cons of finite-element discretizations for nonsmooth variational problems in vision, their multiresolution properties, and the optimization algorithms to solve them. Our 2 and 3D experiments in image segmentation, optical flow, stereo, and depth fusion reveal the conditions where finite-element can outperform finite-difference discretizations by achieving significant computational savings with a minor loss of accuracy.

关键词： Optical Flow Data Term Quadrature Point Polynomial space Pixel Grid these keywords were added by machine and not by the authors. this process is experimental and the keywords may be updated as the learning algorithm improves.

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Directional Total Generalized Variation Regularization for Impulse Noise Removal 6th

Directional Total Generalized Variation Regularization for I...

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

作者： Kongskov, Rasmus Dalgas Dong, Yiqiu Tech Univ Denmark Appl Math & Comp Sci Lyngby Denmark

ISBN: (纸本)9783319587714;9783319587707

A recently suggested regularization method, which combines directional information with total generalized variation (TGV), has been shown to be successful for restoring Gaussian noise corrupted images. We extend the use of this regularizer to impulse noise removal and demonstrate that using this regularizer for directional images is highly advantageous. In order to estimate directions in impulse noise corrupted images, which is much more challenging compared to Gaussian noise corrupted images, we introduce a new Fourier transform-based method. Numerical experiments show that this method is more robust with respect to noise and also more efficient than other direction estimation methods.

关键词： Directional total generalized variation Impulse noise variational methods Regularization Image restoration

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A Unified Hyperelastic Joint Segmentation/Registration Model Based on Weighted Total Variation and Nonlocal Shape Descriptors 6th

A Unified Hyperelastic Joint Segmentation/Registration Model...

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

作者： Debroux, Noemie Le Guyader, Carole Normandie Univ INSA Rouen Lab Math 685 Ave Univ F-76801 St Etienne Du Rouvray France

ISBN: (纸本)9783319587714;9783319587707

In this paper, we address the issue of designing a unified variational model for joint segmentation and registration in which the shapes to be matched are viewed as hyperelastic materials, and more precisely, as Saint Venant-Kirchhoff ones. the dissimilarity measure relates local and global (or region-based) information, since relying on weighted total variation and on a nonlocal shape descriptor inspired by the Chan-Vese model for segmentation. theoretical results emphasizing the mathematical and practical soundness of the model are provided, among which relaxation, existence of minimizers, analysis of two numerical methods of resolution, asymptotic results and a Gamma-convergence property.

关键词： Segmentation Registration Nonlinear elasticity Saint Venant-Kirchhoff material Quasiconvexity Relaxation Weighted BV space Piecewise constant Mumford-Shah model Nonlocal operators Gamma-convergence Asymptotic results

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Optimal Transport for Manifold-Valued Images 6th

Optimal Transport for Manifold-Valued Images

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

作者： Fitschen, Jan Henrik Laus, Friederike Schmitzer, Bernhard Univ Kaiserslautern Dept Math Kaiserslautern Germany Univ Munster Inst Computat & Appl Math Munster Germany

ISBN: (纸本)9783319587714;9783319587707

We introduce optimal transport-type distances for manifold-valued images. To do so we lift the initial data to measures on the product space of image domain and signal space, where they can then be compared by optimal transport with a transport cost that combines spatial distance and signal discrepancy. Applying recently introduced 'unbalanced' optimal transport models leads to more natural results. We illustrate the benefit of the lifting with numerical examples for interpolation of color images and classification of handwritten digits.

关键词： Color space Image Domain Signal space Back Projection Optimal Transport these keywords were added by machine and not by the authors. this process is experimental and the keywords may be updated as the learning algorithm improves.

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Dynamic Texture Recognition Using Time-Causal Spatio-Temporal scale-space Filters 6th

Dynamic Texture Recognition Using Time-Causal Spatio-Tempora...

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

作者： Jansson, Ylva Lindeberg, Tony KTH Royal Inst Technol Computat Brain Sci Lab Dept Computat Sci & Technol Sch Comp Sci & Commun Stockholm Sweden

ISBN: (纸本)9783319587714;9783319587707

this work presents an evaluation of using time-causal scale-space filters as primitives for video analysis. For this purpose, we present a new family of video descriptors based on regional statistics of spatio-temporal scale-space filter responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain. We evaluate one member in this family, constituting a joint binary histogram, on two widely used dynamic texture databases. the experimental evaluation shows competitive performance compared to previous methods for dynamic texture recognition, especially on the more complex Dyn-Tex database. these results support the descriptive power of time-causal spatio-temporal scale-space filters as primitives for video analysis.

关键词： Receptive Field Local Binary Pattern Support Vector Machine Classifier Near Neighbour Linear Dynamical System these keywords were added by machine and not by the authors. this process is experimental and the keywords may be updated as the learning algorithm improves.

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