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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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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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scale space and variational methods in computer vision 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Jean-François Aujol Nicolas Papadakis Mila Nikolova

ISBN: (数字)9783319184616

ISBN: (纸本)9783319184609

this book constitutes the refereed proceedings of the 5th international conference on scale space and variational methods in computer vision, ssvm 2015, held in Lège-Cap Ferret, France, in May 2015. the 56 revised full papers presented were carefully reviewed and selected from 83 submissions. the papers are organized in the following topical sections: scale space and partial differential equation methods; denoising, restoration and reconstruction, segmentation and partitioning; flow, motion and registration; photography, texture and color processing; shape, surface and 3D problems; and optimization theory and methods in imaging.

关键词： Image Processing and computer vision computer Graphics Pattern Recognition Algorithm Analysis and Problem Complexity Information Systems Applications (incl. Internet) Computation by Abstract Devices

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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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A full curvature based algorithm for image registration

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JOURNAL OF MAthEMATICAL IMAGING AND vision 2006年第2期24卷 195-208页

作者： Henn, S Univ Dusseldorf Inst Math D-40225 Dusseldorf Germany

Image registration, i.e., finding an optimal displacement field u which minimizes a distance functional D(u) is known to be an ill-posed problem. In this paper a novel variational image registration method is presented, which matches two images acquired from the same or from different medical imaging modalities. the approach proposed here is also independent of the image dimension. the proposed variational penalty against oscillations in the solutions is the standard H-2(ohm) Sobolev semi-inner product for each component of the displacement. We investigate the associated Euler-Lagrange equation of the energy functional. Furthermore, we approach the solution of the underlying system of biharmonic differential equations with higher order boundary conditions as the steady-state solution of a parabolic partial differential equation (PDE). One of the important aspects of this approach is that the kernel of the Euler-Lagrange equation is spanned by all rigid motions. Hence, the presented approach includes a rigid alignment. Experimental results on both synthetic and real images are presented to illustrate the capabilities of the proposed approach.

关键词： image processing image registration variational methods regularization biharmonic differential-equation

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variational exposure fusion with optimal local contrast 5

Variational exposure fusion with optimal local contrast

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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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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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Artifact-free variational MPEG decompression 5

Artifact-free variational MPEG decompression

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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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Spatio-Temporal scale Selection in Video Data 6th

Spatio-Temporal Scale Selection in Video Data

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

作者： 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

We present a theory and a method for simultaneous detection of local spatial and temporal scales in video data. the underlying idea is that if we process video data by spatio-temporal receptive fields at multiple spatial and temporal scales, we would like to generate hypotheses about the spatial extent and the temporal duration of the underlying spatio-temporal image structures that gave rise to the feature responses. For two types of spatio-temporal scale-space representations, (i) a non-causal Gaussian spatio-temporal scale space for offline analysis of pre-recorded video sequences and (ii) a time-causal and time-recursive spatio-temporal scale space for online analysis of real-time video streams, we express sufficient conditions for spatio-temporal feature detectors in terms of spatio-temporal receptive fields to deliver scale covariant and scale invariant feature responses. A theoretical analysis is given of the scale selection properties of six types of spatio-temporal interest point detectors, showing that five of them allow for provable scale covariance and scale invariance. then, we describe a time-causal and time-recursive algorithm for detecting sparse spatio-temporal interest points from video streams and show that it leads to intuitively reasonable results.

关键词： Temporal scale Video Data Local Extremum scale Covariance scale Selection 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 PDE methods in computer vision - 5th international conference, scale-space 2005, Proceedings

Scale Space and PDE Methods in Computer Vision - 5th Interna...

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5th international conference on scale space and PDE methods in computer vision, scale-space 2005

the proceedings contain 53 papers from the scale space and PDE methods in computer vision - 5th international conference, scale-space 2005, Proceedings. the topics discussed include: regularity and scale-space properties of fractional high order linear filtering;discrete representation of top points via scale-space tessellation;a linear image reconstruction framework based on sobolev type inner products;active shape models and segmentation of the left ventricle in echocardiography;discrete orthogonal decomposition and variational optic flow in real-time;and perfusion analysis of nonlinear liver ultrasound images based on nonlinear matrix diffusion.

关键词： computer vision

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