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38 篇 会议

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检索条件"任意字段=1st International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2007"

共 38 条记录，以下是11-20 订阅

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A variational approach for multi-valued velocity field estimation in transparent sequences

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1st international conference on scale space and variational methods in computer vision

作者： Ramirez-Manzanares, Alonso Rivera, Mariano Kornprobst, Pierre Lauze, Francois Ctr Invest & math AC AP 402 Guanajuato 36000 Mexico INRIA Odyssee Lab Sophia Antipolis France Nord Biosc Imag A S Herlev Denmark

ISBN: (纸本)9783540728221

We propose a variational approach for multi-valued velocity field estimation in transparent sequences. starting from existing local motion estimators, we show a variational model for integrating in space and time these local estimations to obtain a robust estimation of the multi-valued velocity field. With this approach, we can indeed estimate some multi-valued velocity fields which are not necessarily piecewise constant on a layer: Each layer can evolve according to non-parametric optical flow. We show how our approach outperforms some existing approaches, and we illustrate its capabilities on several challenging synthetic/real sequences.

关键词： variational approaches transparent motion multi-valued velocity fields model competition

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Combining different types of scale space interest points using canonical sets

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1st international conference on scale space and variational methods in computer vision, ssvm 2007

作者： Kanters, Frans Denton, Trip Shokoufandeh, Ali Florack, Luc Ter Haar Romeny, Bart Eindhoven University of Technology Den Dolech 2 Postbus 513 5600 MB Eindhoven Netherlands Drexel University University Crossings 141 Philadelphia United States

ISBN: (纸本)9783540728221

scale space interest points capture important photometric and deep structure information of an image. The information content of such points can be made explicit using image reconstruction. In this paper we will consider the problem of combining multiple types of interest points used for image reconstruction. It is shown that ordering the complete set of points by differential (quadratic) TV-norm (which works for single feature types) does not yield optimal results for combined point sets. The paper presents a method to solve this problem using canonical sets of scale space features. Qualitative and quantitative analysis show improved performance over simple ordering of points using the TV-norm. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Image reconstruction

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A variational method with a noise detector for impulse noise removal

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1st international conference on scale space and variational methods in computer vision

作者： Chen, Shoushui Yang, Xin Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China

ISBN: (纸本)9783540728221

In this paper we propose a combined method for removing impulse noise. In the first phase, we use an efficient detector, called the statistics of Ordered Difference Detector (SODD) to identify pixels which are likely to be corrupted by impulse noise. The proposed SODD can yield very high noise detection accuracy at high noise density. This noise detection phase is crucial for the following noise removal. In the second phase, only these noise candidates are restored using the variational method. Edges and noise free pixels of images filtered by our combined method are preserved. Simulation results indicate that the proposed method is significantly better than those using other impulse noise reduction filters.

关键词： Impulse noise

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Spatio-temporal scale-spaces

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1st international conference on scale space and variational methods in computer vision

作者： Fagerstrom, Daniel Royal Inst Technol Dept Numer Anal & Comp Sci Computat Vis & Act Percep Lab SE-10044 Stockholm Sweden

ISBN: (纸本)9783540728221

A family of spatio-temporal scale-spaces suitable for a moving observer is developed. The scale-spaces are required to be time causal for being usable for real time measurements, and to be "velocity adapted";i.e. to have Galilean covariance to avoid favoring any particular motion. Furthermore standard scale-space axioms: linearity, positivity, continuity, translation invariance, scaling covariance in space and time, rotational invariance in space and recursivity are used. An infinitesimal criterion for scale-spaces is developed, which simplifies calculations and makes it possible to define scale spaces on bounded regions. We show that there are no temporally causal Galilean scale-spaces that are semigroups acting on space and time, but that there are such scale-spaces that are semigroups acting on space and memory (where the memory is the scale-space). The temporally causal scale-space is a time-recursive process using current input and the scale-space as state, i.e. there is no need for storing earlier input. The diffusion equation acting on the memory with the input signal as boundary condition, is a member of this family of scale spaces and is special in the sense that its generator is local.

关键词： Motion estimation

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Bounds on the minimizers of (nonconvex) regularized least-squares

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1st international conference on scale space and variational methods in computer vision

作者： Nikolova, Mila PRES UniverSud CNRS ENS Cachan CMLA F-94230 Cachan France

ISBN: (纸本)9783540728221

This is a theoretical study on the minimizers of cost-functions composed of an l(2) data-fidelity term and a possibly nonsmooth or non-convex regularization term acting on the differences or the discrete gradients of the image or the signal to restore. More precisely, we derive general nonasymptotic analytical bounds characterizing the local and the global minimizers of these cost-functions. We provide several bounds relevant to the observation model. For edge-preserving regularization, we exhibit a tight bound on the l(infinity) norm of the residual (the error) that is independent of the data, even if its l(2) norm is being minimized. Then we focus on the smoothing incurred by the (local) minimizers in terms of the differences or the discrete gradient of the restored image (or signal).

关键词： restoration regularization variational methods bounds l(infinity) norm edge preservation inverse problems non-convex analysis nonsmooth analysis

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Convex inverse scale spaces

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1st international conference on scale space and variational methods in computer vision

作者： Frick, Klaus Scherzer, Otmar Univ Innsbruck Dept Comp Sci Technikerstr 21A A-6020 Innsbruck Austria

ISBN: (纸本)9783540728221

Inverse scale space methods are derived as asymptotic limits of iterative regularization methods. They have proven to be efficient methods for denoising of gray valued images and for the evaluation of unbounded operators. In the beginning, inverse scale space methods have been derived from iterative regularization methods with squared Hilbert norm regularization terms, and later this concept was generalized to Bregman distance regularization (replacing the squared regularization norms);therefore allowing for instance to consider iterative total variation regularization. We have proven recently existence of a solution of the associated inverse total variation flow equation. In this paper we generalize these results and prove existence of solutions of inverse flow equations derived from iterative regularization with general convex regularization functionals. We present some applications to filtering of color data and for the stable evaluation of the diZenzo edge detector.

关键词： Inverse problems

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Modeling foveal vision

引用

1st international conference on scale space and variational methods in computer vision

作者： Florack, Luc Eindhoven Univ Technol Dept Biomed Engn NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)9783540728221

A geometric model is proposed for an artificial foveal vision system, and its plausibility in the context of biological vision is explored. The model is based on an isotropic, scale invariant two-form that describes the spatial layout of receptive fields in the the visual sensorium (in the biological context roughly corresponding to retina, LGN, and V1). It overcomes the limitation of the familiar log-polar model by handling its singularity in a graceful way. The log-polar singularity arises as a result of ignoring the physical resolution limitation inherent in any real (artificial or biological) visual system. The incorporation of such a limitation requires the introduction of a physical constant, measuring the radius of the geometric foveola (a central region characterized by maximal resolving power). The proposed model admits a description in singularity-free canonical coordinates that generalize the well-established log-polar coordinates, and that reduce to these in the asymptotic case of negligibly sized geometric foveola (or, equivalently, at peripheral locations in the visual field). Biological plausibility of the model is demonstrated by comparison with known facts on human vision.

关键词： generalized log-polar map foveal vision cortical magnification scale invariance receptive field scaling

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Curve evolution in subspaces

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1st international conference on scale space and variational methods in computer vision

作者： Tatu, Aditya Lauze, Francois Nielsen, Mads Olsen, Ole Fogh Univ Copenhagen DIKU Univ Sparken 1 DK-2100 Copenhagen Denmark Nordic Biosci Clin Stud AS Herlev 2370 Denmark IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9783540728221

Curve evolution forms the basis of active contour algorithms used for image segmentation. In many applications the curve under evolution needs to be restricted to the shape space given by some example shapes, or some linear space given by a set of basis vectors. Also, when a curve evolution is carried out on a computer, the evolution is approximated by some suitable discretization. Here too, the evolution is implicitly carried out in some subspace and not in the space of all curves. Hence it is important to study curve evolution in subspace of all curves. We look at a formulation that describes curve evolution restricted to subspaces. We give numerical methods and examples of a formulation for curvature flow for curves restricted to the B-spline subspace.

关键词： Image segmentation

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The variational origin of motion by Gaussian curvature

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1st international conference on scale space and variational methods in computer vision

作者： Overgaard, Niels Chr. Solem, Jan Erik Malmo Univ Appl Math Grp Sch Technol & Society Malmo Sweden

ISBN: (纸本)9783540728221

A variational formulation of an image analysis problem has the nice feature that it is often easier to predict the effect of minimizing a certain energy functional than to interpret the corresponding Euler-Lagrange equations. For example, the equations of motion for an active contour usually contains a mean curvature term, which we know will regularizes the contour because mean curvature is the first variation of curve length, and shorter curves are typically smoother than longer ones. In some applications it may be worth considering Gaussian curvature as a regularizing term instead of mean curvature. The present paper provides a variational principle for this: We show that Gaussian curvature of a regular surface in three-dimensional Euclidean space is the first variation of an energy functional defined on the surface. Some properties of the corresponding motion by Gaussian curvature are pointed out, and a simple example is given, where minimization of this functional yields a nontrivial solution.

关键词： Total mean curvature Gaussian curvature gradient descent flow level set methods Euler characteristic

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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...

引用

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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