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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21-30 订阅

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A variational framework for the simultaneous segmentation and object Behavior classification of image sequences

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

作者： Gui, Laura Thiran, Jean-Philippe Paragios, Nikos Ecole Polytech Fed Lausanne Signal Proc Inst Lausanne Switzerland Laboratoire MAS Ecole Centrale Paris France

ISBN: (纸本)9783540728221

In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution of the other, thereby enhancing the final result. In particular, classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent with prior knowledge. The prior models are learned from training data and updated dynamically, based on segmentations of earlier images in the sequence. We demonstrate the potential of our approach in a hand gesture recognition application, where the combined use of segmentation and classification improves robustness in the presence of occlusion and background complexity.

关键词： Object recognition

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Beauty with variational methods: An optic flow approach to hairstyle simulation

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

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

ISBN: (纸本)9783540728221

Although variational models offer many advantages in image analysis, their successful application to real-world problems is documented only for some specific areas such as medical imaging. In this paper we show how well-adapted variational ideas can solve the problem of hairstyle simulation in a fully automatic way: A customer in a hairdresser's shop selects a new hairstyle from a database, and this hairstyle is automatically registered to a digital image of the customer's face. Interestingly already a carefully modified optic flow method of Horn and Schunck turns out to be ideal for this application. These modifications include an extension to colour sequences, an incorporation of warping ideas in order to allow large deformation rates, and the inclusion of shape information that is characteristic for human faces. Employing classical numerical ideas such as finite differences and SOR iterations offers sufficient performance for real-life applications. In a number of experiments we demonstrate that our variational approach is capable of solving the hairstyle simulation problem with high quality in a fully practical setting.

关键词： variational techniques

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A variational framework for adaptive satellite images segmentation

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

作者： Besbes, Olfa Belhadj, Ziad Boujemaa, Nozha INRIA Rocquencourt IMEDIA Project F-78153 Le Chesnay France URISA SUPCOM Ariana Tunisia

ISBN: (纸本)9783540728221

In this paper, we present an adaptive variational segmentation algorithm of spectral / texture regions in satellite images using level set. Satellite images contain both textured and non-textured regions, so for each region spectral and texture cues are integrated according to their discrimination power. Motivated by Fisher-Rao linear discriminant analysis;two region weights are defined to code respectively the relevance of spectral and texture cues. Therefore;regions with or without texture are processed in an unified framework. The obtained segmentation criterion is minimized via curves evolution within an explicit correspondence between the interiors of evolving curves and regions in the segmentation. The shape derivation principle is used to derive the system of coupled evolution equations in such a way that we consider the region weights and the statistical parameters variability. Experimental results on both natural and satellite images are shown.

关键词： Image segmentation

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Generic maximum likely scale selection

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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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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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Direct shape-from-shading with adaptive higher order regularisation

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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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Level set methods and the stereo problem 1st

引用

1st international conference on scale-space Theory in computer vision (scale-space 97)

作者： Faugeras, O Keriven, R INRIA Sophia Antipolis France MIT AI Lab Cambridge MA 02139 USA ENPC CERMICS Noisy Le Grand France

ISBN: (纸本)3540631674

We present a novel geometric approach for solving the stereo problem for an arbitrary number of images (greater than or equal to 2). It is based upon the definition of a variational principle that must be satisfied by the surfaces of the objects in the scene and their images. The Euler-Lagrange equations which are deduced from the variational principle provide a set of PDE's which are used to deform an initial set of surfaces which then move towards the objects to be detected. The level set implementation of these PDE's potentially provides an efficient and robust way of achieving the surface evolution and to deal automatically with changes in the surface topology during the deformation, i.e. to deal with multiple objects. Results of a two dimensional implementation of our theory are presented on synthetic and real images.

关键词： Equations of motion

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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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