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共 291 条记录，以下是121-130 订阅

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Learning Filter Functions in Regularisers by Minimising Quotients 1

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

作者： Benning, Martin Gilboa, Guy Grah, Joana Sarah Schonlieb, Carola-Bibiane Univ Cambridge Ctr Math Sci Dept Appl Math & Theoret Phys Wilberforce Rd Cambridge CB3 0WA England Technion Israel Inst Technol Elect Engn Dept IL-32000 Haifa Israel

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

Learning approaches have recently become very popular in the field of inverse problems. A large variety of methods has been established in recent years, ranging from bi-level learning to high-dimensional machine learning techniques. Most learning approaches, however, only aim at fitting parametrised models to favourable training data whilst ignoring misfit training data completely. In this paper, we follow up on the idea of learning parametrised regularisation functions by quotient minimisation as established in [3]. We extend the model therein to include higher-dimensional filter functions to be learned and allow for fit- and misfit-training data consisting of multiple functions. We first present results resembling behaviour of well-established derivative-based sparse regularisers like total variation or higher-order total variation in one-dimension. Our second and main contribution is the introduction of novel families of non-derivative-based regularisers. This is accomplished by learning favourable scales and geometric properties while at the same time avoiding unfavourable ones.

关键词： Regularisation learning Non-linear eigenproblem Sparse regularisation Generalised inverse power method

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A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow 1

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

作者： Maurer, Daniel Stoll, Michael Volz, Sebastian Gairing, Patrick Bruhn, Andres Univ Stuttgart Comp Vis & Intelligent Syst Grp Inst Visualizat & Interact Syst D-70569 Stuttgart Germany

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

In variational optical flow estimation, second order regularisation plays an important role, since it offers advantages in the context of non-fronto-parallel motion. However, in contrast to first order smoothness constraints, most second order regularisers are limited to isotropic concepts. Moreover, the few existing anisotropic concepts are lacking a comparison so far. Hence, our contribution is twofold. (i) First, we juxtapose general concepts for isotropic and anisotropic second order regularization based on direct second order methods, infimal convolution techniques, and indirect coupling models. For all the aforementioned strategies suitable optical flow regularisers are derived. (ii) Second, we show that modelling anisotropic second order smoothness terms gives an additional degree of freedom when penalising deviations from smoothness. This in turn allows us to propose a novel anisotropic strategy which we call double anisotropic regularisation. Experiments on the two KITTI benchmarks show the qualitative differences between the different strategies. Moreover, they demonstrate that the novel concept of double anisotropic regularisation is able to produce excellent results.

关键词： Higher order regularisation variational methods Optical flow

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The Hessian of Axially Symmetric Functions on SE(3) and Application in 3D Image Analysis 1

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

作者： Janssen, Michiel H. J. Dela Haije, Tom C. J. Martin, Frank C. Bekkers, Erik J. Duits, Remco Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

We propose a method for computation of the Hessian of axially symmetric functions on the roto-translation group SE(3). Eigendecomposition of the resulting Hessian is then used for curvature estimation of tubular structures, similar to how the Hessian matrix of 2D or 3D image data can be used for orientation estimation. This paper focuses on a new implementation of a Gaussian regularized Hessian on the roto-translation group. Furthermore we show how eigenanalysis of this Hessian gives rise to exponential curve fits on data on position and orientation (e.g. orientation scores), whose spatial projections provide local fits in 3D data. We quantitatively validate our exponential curve fits by comparing the curvature of the spatially projected fitted curve to ground truth curvature of artificial 3D data. We also show first results on real MRA data.

关键词： Spherical Harmonic Hessian Matrix Orientation Score Curvature Estimation Exponential Curve 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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scale space and variational methods in computer vision: 5th international conference, SSVM 2015 Lège-Cap Ferret, France, May 31 - June 4, 2015 proceedings 5

Scale space and variational methods in computer vision: 5th ...

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5th international conference on scale space and variational methods in computer vision, SSVM 2015

作者： Aujol, Jean-François Nikolova, Mila Papadakis, Nicolas University of Bordeaux Talence France CNRS ENS Cachan Cachan France CNRS University of Bordeaux Talence France

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5th international conference on scale space and variational methods in computer vision, SSVM 2015

5th International Conference on Scale Space and Variational ...

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5th international conference on scale space and variational methods in computer vision, SSVM 2015

ISBN: (纸本)9783319184609

The proceedings contain 56 papers. The special focus in this conference is on scale space and variational methods in computer vision. The topics include: scale-space theory for auditory signals;spectral representations of one-homogeneous functionals;the morphological equivalents of relativistic and alpha-scale-spaces;new approximation of a scale space kernel on SE(3) and applications in neuroimaging;partial differential equations of bivariate median filters;fundamentals of non-local total variation spectral theory;morphological scale-space operators for images supported on point clouds;separable time-causal and time-recursive spatio-temporal receptive fields;a linear scale-space theory for continuous nonlocal evolutions;bilevel image denoising using gaussianity tests;on debiasing restoration algorithms: applications to total-variation and nonlocal-means;cartoon-texture-noise decomposition with transport norms;compressing images with diffusion- and exemplar-based inpainting;some nonlocal filters formulation using functional rearrangements;total variation restoration of images corrupted by poisson noise with iterated conditional expectations;regularization with sparse vector fields: from image compression to TV-type reconstruction;solution-driven adaptive total variation regularization;artifact-free variational MPEG decompression;probabilistic correlation clustering and image partitioning using perturbed multicuts;optimizing the relevance-redundancy tradeoff for efficient semantic segmentation;convex color image segmentation with optimal transport distances;piecewise geodesics for vessel centerline extraction and boundary delineation with application to retina segmentation;unsupervised learning using the tensor voting graph;interactive multi-label segmentation of RGB-D images and fast minimization of region-based active contours using the shape hessian of the energy.

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variational perspective shape from shading 5

Variational perspective shape from shading

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5th international conference on scale space and variational methods in computer vision, SSVM 2015

作者： Ju, Yong Chul Bruhn, Andrés Breuß, Michael Computer Vision and Intelligent Systems Group Institute for Visualization and Interactive Systems University of Stuttgart Universitätsstraße 38 Stuttgart70569 Germany Applied Mathematics and Computer Vision Group Institute for Applied Mathematics and Scientic Computing BTU Cottbus-Senftenberg Platz der Deutschen Einheit 1 Cottbus03046 Germany

ISBN: (纸本)9783319184609

Many recent methods for perspective shape from shading (SfS) are based on formulations in terms of partial differential equations (PDEs). However, while the quality of such methods steadily improves, their lacking robustness is still an open issue. In this context, variational methods seem to be a promising alternative, since they allow to incorporate smoothness assumptions that have proven to be useful formany other tasks in computer vision. Surprisingly, however, suchmethods have hardly been considered for perspective SfS so far. In our article we address this problem and develop a novel variational model for this task. By combining building blocks of recent PDE-based methods such as a Lambertian reflectance model and camera-centred illumination with a discontinuitypreserving second-order smoothness term, we obtain a variational method for perspective SfS that offers by construction an improved degree of robustness compared to existing PDE-based approaches. Our experiments confirm the success of our strategy. They show that embedding the assumptions of PDE-based approaches into a variational model with a suitable smoothness term can be very beneficial – in particular in scenarios with noise or partially missing information. © Springer international Publishing Switzerland 2015.

关键词： Cameras

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Bilevel optimization with nonsmooth lower level problems 5

Bilevel optimization with nonsmooth lower level problems

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5th international conference on scale space and variational methods in computer vision, SSVM 2015

作者： Ochs, Peter Ranftl, René Brox, Thomas Pock, Thomas Computer Vision Group University of Freiburg Freiburg Germany Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Digital Safety and Security Department AIT Austrian Institute of Technology GmbH Vienna1220 Austria

ISBN: (纸本)9783319184609

We consider a bilevel optimization approach for parameter learning in nonsmooth variational models. Existing approaches solve this problem by applying implicit differentiation to a sufficiently smooth approximation of the nondifferentiable lower level problem. We propose an alternative method based on differentiating the iterations of a nonlinear primal–dual algorithm. Our method computes exact (sub)gradients and can be applied also in the nonsmooth setting. We show preliminary results for the case of multi-label image segmentation. © IFIP international Federation for Information Processing 2015.

关键词： Image segmentation

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variational exposure fusion with optimal local contrast 5

Variational exposure fusion with optimal local contrast

引用

5th international conference on scale space and variational methods in computer vision, SSVM 2015

作者： Hafner, David Weickert, Joachim Mathematical Image Analysis Group Saarland University Campus E1.7 Saarbrücken66041 Germany

ISBN: (纸本)9783319184609

In this paper, we present a variational method for exposure fusion. In particular, we combine differently exposed images to a single composite that offers optimal exposedness, saturation, and local contrast. To this end, we formulate the output image as a convex combination of the input, and design an energy functional that implements important perceptually inspired concepts from contrast enhancement such as a local and nonlinear response. Several experiments demonstrate the quality of our technique and show improvements w.r.t. state-of-the-art methods. © Springer international Publishing Switzerland 2015.

关键词： Image enhancement

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Artifact-free variational MPEG decompression 5

Artifact-free variational MPEG decompression

引用

5th international conference on scale space and variational methods in computer vision, SSVM 2015

作者： Bredies, Kristian Holler, Martin Institute for Mathematics and Scientific Computing University of Graz Graz Austria

ISBN: (纸本)9783319184609

We propose a variational method for artifact-free video decompression that is capable of processing any MPEG-2 encoded movie. The method extracts, from a given MPEG-2 file, a set of admissible image sequences and minimizes an artifact-penalizing spatio-temporal regularization functional over this set, giving an optimal decompressed image sequence. For regularization, we use the infimal convolution of spatiotemporal Total Generalized Variation functionals (ICTGV). Numerical experiments on MPEG encoded files show that our approach significantly increases image quality compared to standard decompression. © Springer international Publishing Switzerland 2015.

关键词： Convolution

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Separable time-causal and time-recursive spatio-temporal receptive fields 5

Separable time-causal and time-recursive spatio-temporal rec...

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5th international conference on scale space and variational methods in computer vision, SSVM 2015

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

ISBN: (纸本)9783319184609

We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, obtained by a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about parameterizing the intermediate temporal scale levels, analysing the resulting temporal dynamics and transferring the theory to a discrete implementation in terms of recursive filters over time. © Springer international Publishing Switzerland 2015.

关键词： Artificial intelligence

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