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共 291 条记录，以下是101-110 订阅

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variational Registration of Multiple Images with the SVD Based $$\mathrm {S}q\mathrm {N}$$ Distance Measure

Variational Registration of Multiple Images with the SVD Bas...

引用

7th international conference on scale space and variational methods in computer vision, SSVM 2019

作者： Brehmer, Kai Aggrawal, Hari Om Heldmann, Stefan Modersitzki, Jan Institute of Mathematics and Image Computing University of Lübeck Lübeck Germany Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany

ISBN: (纸本)9783030223670

Image registration, especially the quantification of image similarity, is an important task in image processing. Various approaches for the comparison of two images are discussed in the literature. However, although most of these approaches perform very well in a two image scenario, an extension to a multiple images scenario deserves attention. In this article, we discuss and compare registration methods for multiple images. Our key assumption is, that information about the singular values of a feature matrix of images can be used for alignment. We introduce, discuss and relate three recent approaches from the literature: the Schatten q-norm based (Formula Presented) distance measure, a rank based approach, and a feature volume based approach. We also present results for typical applications such as dynamic image sequences or stacks of histological sections. Our results indicate that the (Formula Presented) approach is in fact a suitable distance measure for image registration. Moreover, our examples also indicate that the results obtained by (Formula Presented) are superior to those obtained by its competitors. © 2019, Springer Nature Switzerland AG.

关键词： Image registration

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Refitting Solutions Promoted by 12 Sparse Analysis Regularizations with Block Penalties

Refitting Solutions Promoted by 12 Sparse Analysis Regulariz...

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7th international conference on scale space and variational methods in computer vision, SSVM 2019

作者： Deledalle, Charles-Alban Papadakis, Nicolas Salmon, Joseph Vaiter, Samuel Department of Electrical and Computer Engineering University of California San Diego La Jolla United States CNRS Univ. Bordeaux IMB UMR 5251 Talence33400 France IMAG Univ. Montpellier CNRS Montpellier France IMB CNRS Université de Bourgogne Dijon21078 France

ISBN: (纸本)9783030223670

In inverse problems, the use of an L12 analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact while keeping information of interest contained in the biased solution. This is done through the use of refitting block penalties that only act on the co-support of the estimation. Based on an analysis of related works in the literature, we propose a new penalty that is well suited for refitting purposes. We also present an efficient algorithmic method to obtain the refitted solution along with the original (biased) solution for any convex refitting block penalty. Experiments illustrate the good behavior of the proposed block penalty for refitting. © 2019, Springer Nature Switzerland AG.

关键词： Inverse problems

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A Search space Strategy for Pedestrian Detection and Localization in World Coordinates 13

A Search Space Strategy for Pedestrian Detection and Localiz...

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13th international Joint conference on computer vision, Imaging and computer Graphics Theory and Applications (VISIGRAPP) / international conference on computer vision Theory and Applications (VISAPP)

作者： Nilsson, Mikael Ahrnbom, Martin Ardo, Hakan Laureshyn, Aliaksei Lund Univ Ctr Math Sci Lund Sweden Lund Univ Dept Technol & Soc Traff & Rd Lund Sweden

ISBN: (纸本)9789897583063

The focus of this work is detecting pedestrians, captured in a surveillance setting, and locating them in world coordinates. Commonly adopted search strategies operate in the image plane to address the object detection problem with machine learning, for example using scale-space pyramid with the sliding windows methodology or object proposals. In contrast, here a new search space is presented, which exploits camera calibration information and geometric priors. The proposed search strategy will facilitate detectors to directly estimate pedestrian presence in world coordinates of interest. Results are demonstrated on real world outdoor collected data along a path in dim light conditions, with the goal to locate pedestrians in world coordinates. The proposed search strategy indicate a mean error under 20 cm, while image plane search methods, with additional processing adopted for localization, yielded around or above 30 cm in mean localization error. This while only observing 3-4% of patches required by the image plane searches at the same task.

关键词： Pedestrian Detection World Coordinates Machine Learning Camera Calibration

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Corner Detection Using the Affine Morphological scale space 1

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

作者： Alvarez, Luis Univ Las Palmas Gran Canaria Dept Informat & Sistemas Campus Tafira Las Palmas Gran Canaria 35017 Spain

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

We introduce a method for corner estimation based on the affine morphological scale space (AMSS). Using some explicit known formula about corner evolution across AMSS, proven by Alvarez and Morales in 1997, we define a morphological cornerness measure based on the expected evolution of an ideal corner across AMSS. We define a new procedure to track the corner motion across AMSS. To evaluate the accuracy of the method we study in details the results for a collection of synthetic corners with angles from 15 to 160 degrees. We also present experiments in real images and we show that the proposed method can also automatically handle the case of multiple junctions.

关键词： Affine scale space Corner detection Morphology

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Adaptive Discretizations for Non-smooth variational vision 1

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

作者： Estellers, Virginia Soatto, Stefano Univ Calif Los Angeles Vis Lab Los Angeles CA 90095 USA

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

variational problems in vision are solved numerically on the pixel lattice because it provides the simplest computational grid to discretize the input images, even though a uniform grid seldom matches the complexity of the solution. To adapt the complexity of the discretization to the solution, it is necessary to adopt finite-element techniques that match the resolution of piecewise polynomial bases to the resolving power of the variational model, but such techniques have been overlooked for nonsmooth variational models. To address this issue, we investigate the pros and cons of finite-element discretizations for nonsmooth variational problems in vision, their multiresolution properties, and the optimization algorithms to solve them. Our 2 and 3D experiments in image segmentation, optical flow, stereo, and depth fusion reveal the conditions where finite-element can outperform finite-difference discretizations by achieving significant computational savings with a minor loss of accuracy.

关键词： Optical Flow Data Term Quadrature Point Polynomial space Pixel Grid 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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An Optimal Transport-Based Restoration Method for Q-Ball Imaging 1

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

作者： Vogt, Thomas Lellmann, Jan Univ Lubeck Inst Math & Image Comp MIC Lubeck Germany

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

We propose a variational approach for edge-preserving total variation (TV)-based regularization of Q-ball data from high angular resolution diffusion imaging (HArdI). While total variation is among the most popular regularizers for variational problems, its application to orientation distribution functions (ODF), as they naturally arise in Q-ball imaging, is not straightforward. We propose to use an extension that specifically takes into account the metric on the underlying orientation space. The key idea is to write the difference quotients in the TV seminorm in terms of the Wasserstein statistical distance from optimal transport. We combine this regularizer with a matchingWasserstein data fidelity term. Using the Kantorovich-Rubinstein duality, the variational model can be formulated as a convex optimization problem that can be solved using a primal-dual algorithm. We demonstrate the effectiveness of the proposed framework on real and synthetic Q-ball data.

关键词： variational methods Total variation Q-ball imaging Wasserstein distance

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Beyond Multi-view Stereo: Shading-Reflectance Decomposition 1

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

作者： Melou, Jean Queau, Yvain Durou, Jean-Denis Castan, Fabien Cremers, Daniel Univ Toulouse UMR CNRS 5505 IRIT Toulouse France Tech Univ Munich Dept Informat Munich Germany Mikros Image Levallois Perret France

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

We introduce a variational framework for separating shading and reflectance from a series of images acquired under different angles, when the geometry has already been estimated by multi-view stereo. Our formulation uses an l(1)-TV variational framework, where a robust photometric-based data term enforces adequation to the images, total variation ensures piecewise-smoothness of the reflectance, and an additional multi-view consistency term is introduced for resolving the arising ambiguities. Optimisation is carried out using an alternating optimisation strategy building upon iteratively reweighted least-squares. Preliminary results on both a synthetic dataset, using various lighting and reflectance scenarios, and a real dataset, confirm the potential of the proposed approach.

关键词： Reflectance Multi-view Shading variational methods

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A Unified Framework for the Restoration of Images Corrupted by Additive White Noise 1

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

作者： Lanza, Alessandro Sciacchitano, Federica Morigi, Serena Sgallari, Fiorella Univ Bologna Dept Math Bologna Italy Univ Genoa Dept Math Genoa Italy

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

We propose a robust variational model for the restoration of images corrupted by blur and the general class of additive white noises. The solution of the non-trivial optimization problem, due to the non-smooth non-convex proposed model, is efficiently obtained by an Alternating Directions Method of Multipliers (ADMM), which in particular reduces the solution to a sequence of convex optimization sub-problems. Numerical results show the potentiality of the proposed model for restoring blurred images corrupted by several kinds of additive white noises.

关键词： variational image restoration Additive white noise Total Variation Non-convex non-smooth optimization ADMM

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Time Discrete Extrapolation in a Riemannian space of Images 1

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

作者： Effland, Alexander Rumpf, Martin Schaefer, Florian Univ Bonn Inst Numer Simulat Bonn Germany CALTECH Pasadena CA 91125 USA

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

The Riemannian metamorphosis model introduced and analyzed in [7,12] is taken into account to develop an image extrapolation tool in the space of images. To this end, the variational time discretization for the geodesic interpolation proposed in [2] is picked up to define a discrete exponential map. For a given weakly differentiable initial image and a sufficiently small initial image variation it is shown how to compute a discrete geodesic extrapolation path in the space of images. The resulting discrete paths are indeed local minimizers of the corresponding discrete path energy. A spatial Galerkin discretization with cubic splines on coarse meshes for image deformations and piecewise bilinear finite elements on fine meshes for image intensity functions is used to derive a fully practical algorithm. The method is applied to real images and image variations recorded with a digital camera.

关键词： Image extrapolation Shape space Elastic registration Exponential map

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Directional Total Generalized Variation Regularization for Impulse Noise Removal 1

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

作者： Kongskov, Rasmus Dalgas Dong, Yiqiu Tech Univ Denmark Appl Math & Comp Sci Lyngby Denmark

ISBN: (数字)9783319587714

ISBN: (纸本)9783319587714;9783319587707

A recently suggested regularization method, which combines directional information with total generalized variation (TGV), has been shown to be successful for restoring Gaussian noise corrupted images. We extend the use of this regularizer to impulse noise removal and demonstrate that using this regularizer for directional images is highly advantageous. In order to estimate directions in impulse noise corrupted images, which is much more challenging compared to Gaussian noise corrupted images, we introduce a new Fourier transform-based method. Numerical experiments show that this method is more robust with respect to noise and also more efficient than other direction estimation methods.

关键词： Directional total generalized variation Impulse noise variational methods Regularization Image restoration

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