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共 291 条记录，以下是81-90 订阅

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Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers 1

引用

7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Hansen, Jacob Daniel Kirstejn Lauze, Francois Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

This paper presents a 2D and 3D variational segmentation approach based on a similarity invariant, i.e., translation, scaling, and rotation invariant shape regulariser. Indeed, shape moments of order up to 2 for shapes with limited symmetries can be combined to provide a shape normalisation for the group of similarities. In order to obtain a segmentation objective function, a two-means or two-local-means data term is added to it. Segmentation is then obtained by standard gradient descent on it. We demonstrate the capabilities of the approach on a series of experiments, of different complexity levels. We specifically target rat brain shapes in MR scans, where the setting is complex, because of bias field and complex anatomical structures. Our last experiments show that our approach is indeed capable of recovering brain shapes automatically.

关键词： Gradient methods

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Alternate Structural-Textural Video Inpainting for Spot Defects Correction in Movies 1

引用

7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Renaudeau, Arthur Lauze, Francois Pierre, Fabien Aujol, Jean-Francois Durou, Jean-Denis Univ Toulouse UMR CNRS 5505 IRIT Toulouse France Univ Copenhagen DIKU Copenhagen Denmark Univ Lorraine UMR CNRS 7503 LORIA Inria Projet Magrit Nancy France Univ Bordeaux Bordeaux INP CNRS IMBUMR 5251 F-33400 Talence France

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

We propose a new video inpainting model for movies restoration application. Our model combines structural reconstruction with a diffusion-based method and textural reconstruction with a patch-based method. Both proposed energies (one for each method) are alternatively minimized in order to preserve the overall structure while adding textural refinement. While the structural reconstruction is obtained jointly with optical flow computation with several proximal approaches, the textural reconstruction is processed by a variational non-local approach. Preliminary results on different Middlebury frames show quality improvement in the reconstruction.

关键词： computer vision

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A Splitting-Based Algorithm for Multi-view Stereopsis of Textureless Objects 1

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7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Melou, Jean Queau, Yvain Castan, Fabien Durou, Jean-Denis Univ Toulouse IRIT UMR CNRS 5505 Toulouse France GREYC UMR CNRS 6072 Caen France Mikros Image Paris France

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

We put forward a simple, yet effective splitting strategy for multi-view stereopsis. It recasts the minimization of the classic photo-consistency+ gradient regularization functional as a sequence of simple problems which can be solved efficiently. This framework is able to handle various photo-consistency measures and regularization terms, and can be used for instance to estimate either a minimal-surface or a shading-aware solution. The latter makes the proposed approach very effective for dealing with the well-known problem of textureless objects 3D-reconstruction.

关键词： Multi-view stereo 3D-reconstruction Shape-from-shading

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A New Iterative Method for CT Reconstruction with Uncertain View Angles 1

引用

7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Riis, Nicolai Andre Brogaard Dong, Yiqiu Tech Univ Denmark Dept Appl Math & Comp Sci Richard Petersens Pl DK-2800 Lyngby Denmark

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

In this paper, we propose a new iterative algorithm for Computed Tomography (CT) reconstruction when the problem has uncertainty in the view angles. The algorithm models this uncertainty by an additive model-discrepancy term leading to an estimate of the uncertainty in the likelihood function. This means we can combine state-of-the-art regularization priors such as total variation with this likelihood. To achieve a good reconstruction the algorithm alternates between updating the CT image and the uncertainty estimate in the likelihood. In simulated 2D numerical experiments, we show that our method is able to improve the relative reconstruction error and visual quality of the CT image for the uncertain-angle CT problem.

关键词： Computed Tomography Uncertain view angles Model error variational methods Total variation Model discrepancy

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Sparsification scale-spaces 1

引用

7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Cardenas, Marcelo Peter, Pascal Weickert, Joachim Univ Saarland Fac Math & Comp Sci Math Image Anal Grp Campus E1 7 D-66041 Saarbrucken Germany

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

We introduce a novel scale-space concept that is inspired by inpainting-based lossy image compression and the recent denoising by inpainting method of Adam et al. (2017). In the discrete setting, the main idea behind these so-called sparsification scale-spaces is as follows: Starting with the original image, one subsequently removes a pixel until a single pixel is left. In each removal step the missing data are interpolated with an inpainting method based on a partial differential equation. We demonstrate that under fairly mild assumptions on the inpainting operator this general concept indeed satisfies crucial scale-space properties such as gradual image simplification, a discrete semigroup property or invariances. Moreover, our experiments show that it can be tailored towards specific needs by selecting the inpainting operator and the pixel sparsification strategy in an appropriate way. This may lead either to uncommitted scale-spaces or to highly committed, image-adapted ones.

关键词： scale-space Sparsification Inpainting Diffusion Image compression

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A Balanced Phase Field Model for Active Contours 1

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7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Molnar, Jozsef Tasnadi, Ervin Horvath, Peter Hungarian Acad Sci Biol Res Ctr Szeged Hungary Univ Helsinki Inst Mol Med Finland Helsinki Finland

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

In this paper we present a balanced phase field model that eliminates the often undesired curvature-dependent shrinking of the zero level set, while maintaining the smooth interface necessary to calculate fundamental quantities such as the normal vector or the curvature of the represented contour. The proposed model extends the Ginzburg-Landau phase field energy with a higher order smoothness term. The relative weights are determined with the analysis of the level set motion in a curvilinear system adapted to the zero level set. The proposed level set framework exhibits strong shape maintaining capability without significant interference with the active (e.g. a segmentation) model.

关键词： Numerical methods

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A variational Perspective on the Assignment Flow 1

引用

7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Savarino, Fabrizio Schnoerr, Christoph Heidelberg Univ Image & Pattern Anal Grp Heidelberg Germany

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

The image labeling problem can be described as assigning to each pixel a single element from a finite set of predefined labels. Recently, a smooth geometric approach for inferring such label assignments was proposed by following the Riemannian gradient flow of a given objective function on the so-called assignment manifold. Due to the specific Riemannian structure, this results in a coupled replicator dynamic incorporating local spatial geometric averages of lifted data-dependent distances. However, in this framework an approximation of the flow is necessary in order to arrive at explicit formulas. We propose an alternative variational model, where lifting and averaging are decoupled in the objective function so as to stay closer to established approaches and at the same time preserve the main ingredients of the original approach: the overall smooth geometric setting and regularization through geometric local averages. As a consequence the resulting flow is explicitly given, without the need for any approximation. Furthermore, there exists an interesting connection to graphical models.

关键词： Image labeling Assignment manifold Assignment flow Geometric optimization Riemannian gradient flow Replicator equation Multiplicative updates

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A variational Convex Hull Algorithm 1

引用

7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Li, Lingfeng Luo, Shousheng Tai, Xue-Cheng Yang, Jiang Southern Univ Sci & Technol Dept Math Shenzhen Guangdong Peoples R China Hong Kong Baptist Univ Dept Math Kowlong Tong Hong Kong Peoples R China Beijing Computat Sci Res Ctr Beijing Peoples R China Henan Univ Data Anal Technol Lab Sch Math & Stat Kaifeng Peoples R China

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

Finding the convex hull of a given object or a point set is a very important problem. In this paper, we propose a variational convex hull model and numerical algorithms to solve it. Our model is based on level set representation. Efficient numerical algorithms and implementations based on splitting ideas are given. To test our proposed model, we conduct many experiments for objects represented by binary images, and the results suggest that our model can identify the convex hull accurately. Even more, our model can be easily modified to handle the outliers, and this is also demonstrated by numerical examples.

关键词： Convex hull Level set method Image processing Outliers detection

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A Connection Between Image Processing and Artificial Neural Networks Layers Through a Geometric Model of Visual Perception 1

引用

7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Batard, Thomas Ramon Maldonado, Eduard Steidl, Gabriele Bertalmio, Marcelo Crisalix Barcelona Spain Univ Politecn Cataluna Barcelona Spain TU Kaiserslautern Kaiserslautern Germany Univ Pompeu Fabra Barcelona Spain

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

In this paper, we establish a connection between image processing, visual perception, and deep learning by introducing a mathematical model inspired by visual perception from which neural network layers and image processing models for color correction can be derived. Our model is inspired by the geometry of visual perception and couples a geometric model for the organization of some neurons in the visual cortex with a geometric model of color perception. More precisely, the model is a combination of a Wilson-Cowan equation describing the activity of neurons responding to edges and textures in the area V1 of the visual cortex and a Retinex model of color vision. For some particular activation functions, this yields a color correction model which processes simultaneously edges/textures, encoded into a Riemannian metric, and the color contrast, encoded into a nonlocal covariant derivative. Then, we show that the proposed model can be assimilated to a residual layer provided that the activation function is nonlinear and to a convolutional layer for a linear activation function. Finally, we show the accuracy of the model for deep learning by testing it on the MNIST dataset for digit classification.

关键词： Differential geometry variational model Image processing vision Neural network

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A Total Variation Based Regularizer Promoting Piecewise-Lipschitz Reconstructions 1

引用

7th international conference on scale space and variational methods in computer vision (SSVM)

作者： Burger, Martin Korolev, Yury Schonlieb, Carola-Bibiane Stollenwerk, Christiane Univ Erlangen Nurnberg Dept Math Cauerstr 11 D-91058 Erlangen Germany Univ Cambridge Dept Appl Math & Theoret Phys Wilberforce Rd Cambridge CB3 0WA England Univ Munster Inst Anal & Numer Einsteinstr 62 D-48149 Munster Germany

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

We introduce a new regularizer in the total variation family that promotes reconstructions with a given Lipschitz constant (which can also vary spatially). We prove regularizing properties of this functional and investigate its connections to total variation and infimal convolution type regularizers TVLp and, in particular, establish topological equivalence. Our numerical experiments show that the proposed regularizer can achieve similar performance as total generalized variation while having the advantage of a very intuitive interpretation of its free parameter, which is just a local estimate of the norm of the gradient. It also provides a natural approach to spatially adaptive regularization.

关键词： Total variation Total generalized variation First order regularization Image denoising

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