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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21-30 订阅

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Generic maximum likely scale selection

引用

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

作者： Pedersen, Kim steenstrup Loog, Marco Markussen, Bo Univ Copenhagen Dept Comp Sci Copenhagen Denmark Nordic Biosci AS Herlev Denmark Royal Vet & Agr Univ Dept Nat Sci DK-1870 Frederiksberg Denmark

ISBN: (纸本)9783540728221

The fundamental problem of local scale selection is addressed by means of a novel principle, which is based on maximum likelihood estimation. The principle is generally applicable to a broad variety of image models and descriptors, and provides a generic scale estimation methodology. The focus in this work is on applying this selection principle under a Brownian image model. This image model provides a simple scale invariant prior for natural images and we provide illustrative examples of the behavior of our scale estimation on such images. In these illustrative examples, estimation is based on second order moments of multiple measurements outputs at a fixed location. These measurements, which reflect local image structure, consist in the cases considered here of Gaussian derivatives taken at several scales and/or having different derivative orders.

关键词： Maximum likelihood estimation

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Solving the Chan-Vese model by a multiphase level set algorithm based on the topological derivative

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

作者： He, Lin Osher, stanley Johann Radon Inst Computat & Appl Math A-4040 Linz Austria Univ Calif Los Angeles Dept Mat Los Angeles CA 90095 USA

ISBN: (纸本)9783540728221

In this work, we specifically solve the Chan-Vese active contour model by multiphase level set methods. We first develop a fast algorithm based on calculating the variational energy of the Chan-Vese model without the length term. We check whether the energy decreases or not when we move a point to another segmented region. Then we draw a connection between this algorithm and the topological derivative, a concept emerged from the shape optimization field. Furthermore, to include the length terra of the Chan-Vese model;we apply a preprocessing step on the image by using nonlinear diffusion. We show numerical experiments to demonstrate the efficiency and the robustness of our algorithm.

关键词： image segmentation level set methods Chan-Vese model topological derivative

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A variational approach for 3D motion estimation of incompressible PIV flows

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

作者： Alvarez, Luis Castano, Carlos Garcia, Miguel Krissian, Karl Mazorra, Luis Salgado, Agustin Sanchez, Javier Univ Las Palmas Gran Canaria Dept Informat & Sistemas Las Palmas Gran Canaria Spain

ISBN: (纸本)9783540728221

Estimation of motion has many applications in fluid analysis. Lots of work has been carried out using Particle Image Velocimetry to design experiments which capture and measure the flow motion using 2D images. Recent technological advances allow capturing 3D PIV image sequences of moving particles. In this context, we propose a new three-dimensional variational (energy-based) technique. Our technique is based on solenoidal projection to take into account the incompressibility of the real flow. It uses the result of standard flow motion estimation techniques like iterative cross-correlation or pyramidal optical flow as an initialization, and improves significantly their accuracies. The performance of the proposed technique is measured and illustrated using numerical simulations.

关键词： Motion estimation

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Direct shape-from-shading with adaptive higher order regularisation

引用

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

作者： Vogel, Oliver Bruhn, Andres Weickert, Joachim Didas, stephan Univ Saarland Fac Math & Comp Sci Math Image Anal Grp Bldg E1-1 D-66041 Saarbrucken Germany

ISBN: (纸本)9783540728221

Although variational methods are popular techniques in the context of shape-from-shading, they are in general restricted to indirect approaches that only estimate the gradient of the surface depth. Such methods suffer from two drawbacks: (i) They need additional constraints to enforce the integrability of the solution. (ii) They require the application of depth-from-gradient algorithms to obtain the actual surface. In this paper we present three novel approaches that avoid the aforementioned drawbacks by construction: (i) First, we present a method that is based on homogeneous higher order regularisation. Thus it becomes possible to estimate the surface depth directly by solving a single partial differential equation. (ii) Secondly, we develop a refined technique that adapts this higher order regularisation to semantically important structures in the original image. This addresses another drawback of existing variational methods: the blurring of the results due to the regularisation. (iii) Thirdly, we present an even further improved approach, in which the smoothness process is steered directly by the evolving depth map. This in turn allows to tackle the well-known problem of spontaneous concave-convex switches in the solution. In our experimental section both qualitative and quantitative experiments on standard shape-from-shading data sets are performed. A comparison to the popular variational method of Frankot and Chellappa shows the superiority of all three approaches.

关键词： computer vision shape-from-shading variational methods partial differential equations

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Combining curvature motion and edge-preserving denoising

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

作者： Didas, stephan Weickert, Joachim Univ Saarland Dept Math & Comp Sci Math Image Anal Grp Bldg E1 1 D-66041 Saarbrucken Germany

ISBN: (纸本)9783540728221

In this paper we investigate a family of partial differential equations (PDEs) for image processing which can be regarded as isotropic nonlinear diffusion with an additional factor on the right-hand side. The one-dimensional analogues to this filter class have been motivated as scaling limits of one-dimensional adaptive averaging schemes. In 2-D, mean curvature motion is one of the most prominent examples of this family of PDEs. Other representatives of the filter class combine properties of curvature motion with the enhanced edge preservation of Perona-Malik diffusion. It becomes appearent that these PDEs require a careful discretisation. Numerical experiments display the differences between Perona-Malik diffusion, classical mean curvature motion and the proposed extensions. We consider, for example, enhanced edge sharpness, the question of morphological invariance, and the behaviour with respect to noise.

关键词： Edge detection

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Restoration of images with piecewise space-variant blur

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

作者： Bar, Leah Sochen, Nir Kiryati, Nahum Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Tel Aviv Univ Dept Math Appl IL-69978 Tel Aviv Israel Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel

ISBN: (纸本)9783540728221

We address the problem of space-variant image deblurring, where different parts of the image are blurred by different blur kernels. Assuming a region-wise space variant point spread function, we first solve the problem for the case of known blur kernels and known boundaries between the different blur regions in the image. We then generalize the method to the challenging case of unknown boundaries between the blur domains. Using variational and level set techniques, the image is processed globally. The space-variant deconvolution process is stabilized by a unified common regularizer, thus preserving discontinuities between the differently restored image regions. In the case where the blurred sub-regions are unknown, a segmentation procedure is performed using an evolving level set function, guided by edges and image derivatives.

关键词： Image reconstruction

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3D object recognition by eigen-scale-space of contours

引用

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

作者： Lee, Tim K. Drew, Mark S. British Columbia Canc Res Ctr Canc Control Res Program 675 W 10th Ave Vancouver BC V5Z 1L3 Canada Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9783540728221

People often recognize 3D objects by their boundary shape. Designing an algorithm for such a task is interesting and useful for retrieving objects from a shape database. In this paper, we present a fast 2-stage algorithm for recognizing 3D objects using a new feature space, built from curvature scale space images, as a shape representation that is scale, translation, rotation and reflection invariant. As well, the new shape representation removes the inherent ambiguity of the zero position of arc length for a scale space image. The 2-stage matching algorithm, conducted in the eigenspaces of the feature space, is analogous to the way people recognize an object: first identifying the type of object, and then determining the actual object. We test the new algorithm on a 3D database comprising 209 colour objects in 2926 different view poses, and achieve a 97% recognition rate for the object type and 95% for the object pose.

关键词： object recognition shape 3D object scale-space filtering eigen-analysis

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space-time segmentation based on a joint entropy with estimation of Nonparametric distributions

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

作者： Herbulot, Ariane Boltz, Sylvain Debreuve, Eric Barlaud, Michel Aubert, Gilles Univ Nice Sophia Antipolis CNRS Lab I3S Nice France Univ Nice Sophia Antipolis CNRS Lab Dieudonne Nice France

ISBN: (纸本)9783540728221

This paper deals with video segmentation based on motion and spatial information. Classically;the nucleus of the motion term is the motion compensation error (NICE) between two consecutive frames. Defining a motion-based energy as the integral of a, function of the NICE over the object domain implicitly results in making an assumption on the MCE distribution: Gaussian for the square function, Laplacian for the absolute value, or other parametric distributions for functions used in robust estimation. However, these assumptions are generally false. Instead, it is proposed to integrate a function of (an estimation of) the NICE distribution. The function is taken such that the integral is the Ahmad-Lin entropy of the MCE, the purpose being to be more robust to outliers. Since a motion-only approach can fail in homogeneous areas, the proposed energy is the joint entropy of the NICE and the object color. It is minimized using active contours.

关键词： space-time segmentation joint entropy active contour

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scale space and variational methods in computer vision - First international conference, SSVM 2007, Proceedings

引用

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

ISBN: (纸本)9783540728221

The proceedings contain 79 papers. The topics discussed include: iterated nonlocal means for texture restoration;the jet metric;a nonconvex model to remove multiplicative noise;discrete regularization on weighted graphs for image and mesh filtering;segmentation under occlusions using selective shape prior;dense optical flow estimation from the monogenic curvature tensor;some remarks on perspective shape-from-shading models;scale spaces on lie groups;convex inverse scale spaces;the variational origin of motion by Gaussian curvature;numerical invariantization for morphological PDE schemes;combining curvature motion and edge-preserving denoising;non-negative sparse modeling of textures;blur invariant image priors;region based image segmentation using a modified Mumford-Shah algorithm;motion compensated video super resolution;towards segmentation based on a shape prior manifold;and modeling foveal vision.

关键词： computer vision

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scale spaces on lie groups

引用

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

作者： Duits, Remco Burgeth, Bernhard Eindhoven Univ Technol Dept Biomed Engn NL-5600 MB Eindhoven Netherlands Eindhoven Univ Technol Dept Appl Math & Comp Sci NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)9783540728221

In the standard scale space approach one obtains a scale space representation u : R(d) x R(+) -> R of an image f epsilon L(2) (R(d)) by means of an evolution equation on the additive group (R(d), +). However, it is common to apply a wavelet transform (constructed via a representation U of a Lie-group G and admissible wavelet psi) to an image which provides a detailed overview of the group structure in an image. The result of such a wavelet transform provides a function g -> (U(g)psi, f)(L2(R)(2)()) on a group G (rather than (R(d), +)), which we call a score. Since the wavelet transform is unitary we have stable reconstruction by its adjoint. This allows us to link operators on images to operators on scores in a robust way. To ensure U-invariance of the corresponding operator on the image the operator on the wavelet transform must be left-invariant. Therefore we focus on left-invariant evolution equations (and their resolvents) on the Lie-group G generated by a quadratic form Q on left invariant vector fields. These evolution equations correspond to stochastic processes on G and their solution is given by a group convolution with the corresponding Green's function, for which we present an explicit derivation in two particular image analysis applications. In this article we describe a general approach how the concept of scale space can be extended by replacing the additive group R(d) by a Lie-group with more structure.

关键词： variational techniques

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