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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11-20 订阅

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the Fractional Harris-Laplace Feature Detector 7th

The Fractional Harris-Laplace Feature Detector

引用

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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scale space operators on hierarchies of segmentations

引用

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

作者： Kiran, B. Ravi Serra, Jean Université Paris-Est Laboratoire d'Informatique Gaspard-Monge ESIEE France

ISBN: (纸本)9783642382666

A hierarchy of segmentations(partitions) is a multiscale set representation of the image. this paper introduces a new set of scale space operators or transformations on the space of hierarchies of partitions. An ordering of hierarchies is proposed which is endowed by an ω-ordering based on a global energy over the classes of the hierarchy. A class of Matheron semigroups are shown to exists in this ordering of hierarchies. A second contribution is the saliency transformation which fuses a saliency function corresponding to a hierarchy, with an external function, rendering a new or transformed saliency function. the results are demonstrated on the Berkeley dataset. © 2013 Springer-Verlag.

关键词： Image segmentation

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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

引用

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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Image matching using generalized scale-space interest points

引用

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

作者： Lindeberg, Tony School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9783642382666

the performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the associated image descriptors. this paper demonstrates the advantages of using generalized scale-space interest point detectors when computing image descriptors for image-based matching. these generalized scale-space interest points are based on linking of image features over scale and scale selection by weighted averaging along feature trajectories over scale and allow for a higher ratio of correct matches and a lower ratio of false matches compared to previously known interest point detectors within the same class. Specifically, it is shown how a significant increase in matching performance can be obtained in relation to the underlying interest point detectors in the SIFT and the SURF operators. We propose that these generalized scale-space interest points when accompanied by associated scale-invariant image descriptors should allow for better performance of interest point based methods for image-based matching, object recognition and related vision tasks. © 2013 Springer-Verlag.

关键词： Object recognition

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Directional Total Generalized Variation Regularization for Impulse Noise Removal 6th

Directional Total Generalized Variation Regularization for I...

引用

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...

引用

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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Targeted iterative filtering

引用

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

作者： Åström, Freddie Felsberg, Michael Baravdish, George Lundström, Claes Computer Vision Laboratory Linköping University Sweden Center for Medical Image Science and Visualization Linköping University Sweden Department of Science and Technology Linköping University Sweden Sectra AB Sweden

ISBN: (纸本)9783642382666

the assessment of image denoising results depends on the respective application area, i.e. image compression, still-image acquisition, and medical images require entirely different behavior of the applied denoising method. In this paper we propose a novel, nonlinear diffusion scheme that is derived from a linear diffusion process in a value space determined by the application. We show that application-driven linear diffusion in the transformed space compares favorably with existing nonlinear diffusion techniques. © 2013 Springer-Verlag.

关键词： Image denoising

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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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