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共 291 条记录，以下是51-60 订阅

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Equivariant Deep Learning via Morphological and Linear scale space PDEs on the space of Positions and Orientations 8th

Equivariant Deep Learning via Morphological and Linear Scale...

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8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Duits, Remco Smets, Bart Bekkers, Erik Portegies, Jim Department Mathematics and Computer Science TU/e Eindhoven Netherlands Informatics Institute UvA Amsterdam Netherlands

ISBN: (纸本)9783030755485

We present PDE-based Group Convolutional Neural Networks (PDE-G-CNNs) that generalize Group equivariant Convolutional Neural Networks (G-CNNs). In PDE-G-CNNs a network layer is a set of PDE-solvers where geometrically meaningful PDE-coefficients become trainable weights. The underlying PDEs are morphological and linear scale space PDEs on the homogeneous space Md of positions and orientations. They provide an equivariant, geometrical PDE-design and model interpretability of the network. The network is implemented by morphological convolutions with approximations to kernels solving morphological α -scale-space PDEs, and to linear convolutions solving linear α -scale-space PDEs. In the morphological setting, the parameter α regulates soft max-pooling over balls, whereas in the linear setting the cases α= 1 / 2 and α= 1 correspond to Poisson and Gaussian scale spaces respectively. We show that our analytic approximation kernels are accurate and practical. We build on techniques introduced by Weickert and Burgeth who revealed a key isomorphism between linear and morphological scale spaces via the Fourier-Cramér transform. It maps linear α -stable Lévy processes to Bellman processes. We generalize this to Md and exploit this relation between linear and morphological scale-space kernels. We present blood vessel segmentation experiments that show the benefits of PDE-G-CNNs compared to state-of-the-art G-CNNs: increase of performance along with a huge reduction in network parameters. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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Crossing-Preserving Geodesic Tracking on Spherical Images 10th

Crossing-Preserving Geodesic Tracking on Spherical Images

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10th international conference on scale space and variational methods in computer vision, SSVM 2025

作者： van den Berg, Nicky J. Sherry, Finn M. Berendschot, Tos T. J. M. Duits, Remco CASA Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands EAISI Eindhoven University of Technology Eindhoven Netherlands Ophthalmology Department Maastricht University Maastricht Netherlands

ISBN: (纸本)9783031923685

In image analysis one often encounters spherical images, for instance in retinal imaging. The behavior of the vessels in the retina is an indicator of several diseases. To automate disease diagnosis using retinal images, it is necessary to develop an algorithm that automatically identifies and tracks vessels. To deal with crossings due to projected blood vessels in the image it is common to lift retinal images to the space of planar positions and orientations M2:=R2×S1. This implicitly assumes that the flat image accurately represents the geometry of the retina. As the eyeball is a sphere (and not a plane), we propose to compute the cusp-free, crossing-preserving geodesics in the space of spherical positions and orientations W2 on wide-field images. We clarify how to relate both manifolds and compare the calculated geodesics. The results show clear advantages of crossing-preserving tracking in W2 over non-crossing-preserving tracking in W2 and are comparable to tracking results in M2. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Geometry

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Deceptive Diffusion: Generating Synthetic Adversarial Examples 10th

Deceptive Diffusion: Generating Synthetic Adversarial Exampl...

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10th international conference on scale space and variational methods in computer vision, SSVM 2025

作者： Beerens, Lucas Higham, Catherine F. Higham, Desmond J. School of Mathematics University of Edinburgh EdinburghEH9 3FD United Kingdom Maxwell Institute University of Edinburgh EdinburghEH8 9BT United Kingdom School of Computing Science University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783031923654

We introduce the concept of deceptive diffusion—training a generative AI model to produce adversarial images. Whereas a traditional adversarial attack algorithm aims to perturb an existing image to induce a misclassificaton, the deceptive diffusion model can create an arbitrary number of new, misclassified images that are not directly associated with training or test images. Deceptive diffusion offers the possibility of strengthening defence algorithms by providing adversarial training data at scale, including types of misclassification that are otherwise difficult to find. In our experiments, we also investigate the effect of training on a partially attacked data set. This highlights a new type of vulnerability for generative diffusion models: if an attacker is able to stealthily poison a portion of the training data, then the resulting diffusion model will generate a similar proportion of misleading outputs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stabilization

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Extension of Mathematical Morphology in Riemannian spaces 8th

Extension of Mathematical Morphology in Riemannian Spaces

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8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Diop, El Hadji S. Mbengue, Alioune Manga, Bakary Seck, Diaraf Department of Mathematics University Iba Der Thiam Thies Senegal Department of Mathematics and Computer Science University Cheikh Anta Diop Dakar Senegal LMDAN NLAGA University Cheikh Anta Diop Dakar Senegal

ISBN: (纸本)9783030755485

Mathematical morphology remains an efficient image analysis tool due to its morphological scale-spaces capability. It can be formulated using partial differential equations that are in fact a particular case of the first order Hamilton-Jacobi equations for which viscosity solutions exist and are given by Hopf-Lax-Oleinik (HLO) formulas. In this study, we propose an extension of HLO formulas in Riemannian manifolds by considering a general Cauchy problem, and prove the existence of a unique viscosity solution. Some properties are derived, and example on the hyperbolic ball is also provided. © 2021, Springer Nature Switzerland AG.

关键词： Mathematical morphology

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Lightweight neural network based small space debris saliency detection in video 73

Lightweight neural network based small space debris saliency...

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73rd international Astronautical Congress, IAC 2022

作者： Tao, Jiang Cao, Yunfeng Ding, Meng College of Astronautics Nanjing University of Aeronautics and Astronautics 29 Jiangjun Road Nanjing211106 China College of Civil Aviation Nanjing University of Aeronautics and Astronautics 29 Jiangjun Road Nanjing211106 China

The increasing space activities have resulted in numerous space debris, which greatly threatened the space asserts and astronauts's safety. To ensure the safety of human space operation, space-based space debris surveillance is crucial for the emergency avoidance of spacecraft in advance. With the progress of computer vision technology, space debris detection using visual sensors has becoming a favorable solution. However, detecting space debris in the distance is challenging, due to its slow movement relative to the space-based surveillance platform and various noise caused by statistic nature of photodetection in sensor systems. Therefore, it's difficult to discern the space debris in a static surveillance image full of noise and stars. Since the video images contain not only the spatial information in a static image but also the temporal-related information in sequence images, a convolutional neural network based small space debris saliency detection algorithm (SDebrisNet) is studied in this paper. First, a MobileDets based space debris feature extraction structure is constructed, which makes the overall model more lightweight to match the resource-restricted space-based surveillance devices. In particular, an enhanced spatial feature module is proposed to strengthen the spatial details of small object. Then, on the basis of RCRNet, a non-locally enhanced recurrent (NER) module is applied to output the resulting saliency maps. Finally, a small space debris dataset is constructed for algorithm evaluation. Experiment results demonstrate that the proposed method in this paper is robust for detecting small space debris with low signal noise ratio. Copyright © 2022 by Mr. Jiang Tao. Published by the IAF, with permission and released to the IAF to publish in all forms.

关键词： space debris

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Learning Local Regularization for variational Image Restoration 8th

Learning Local Regularization for Variational Image Restora...

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8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Prost, Jean Houdard, Antoine Almansa, Andrés Papadakis, Nicolas Univ. Bordeaux Bordeaux INP CNRS IMB UMR 5251 Talence33400 France Université de Paris MAP5 CNRS Paris75006 France

ISBN: (纸本)9783030755485

In this work, we propose a framework to learn a local regularization model for solving general image restoration problems. This regularizer is defined with a fully convolutional neural network that sees the image through a receptive field corresponding to small image patches. The regularizer is then learned as a critic between unpaired distributions of clean and degraded patches using a Wasserstein generative adversarial networks based energy. This yields a regularization function that can be incorporated in any image restoration problem. The efficiency of the framework is finally shown on denoising and deblurring applications. © 2021, Springer Nature Switzerland AG.

关键词： Image reconstruction

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Local Culprits of Shape Complexity 8th

Local Culprits of Shape Complexity

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8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Arslan, Mazlum Ferhat Tari, Sibel Department of Computer Engineering Middle East Technical University Ankara06800 Turkey

ISBN: (纸本)9783030755485

Quantifying shape complexity is useful in several practical problems in addition to being interesting from a theoretical point of view. In this paper, instead of assigning a single global measure of complexity, we propose a distributed coding where to each point on the shape domain a measure of its contribution to complexity is assigned. We define the shape simplicity as the expressibility of the shape via a prototype shape. To keep discussions concrete we focus on a case where the prototype is a rectangle. Nevertheless, the constructions in the paper is valid in higher dimensions where the prototype is a hyper-cuboid. Thanks to the connection between differential operators and mathematical morphology, the proposed construction naturally extends to the case where diamonds serve as the prototypes. © 2021, Springer Nature Switzerland AG.

关键词： Mathematical morphology

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Bi-level Optimization and Implicit Differentiation as a Framework for Optimal Experimental Design in Tomography

Bi-level Optimization and Implicit Differentiation as a F...

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10th international conference on scale space and variational methods in computer vision, SSVM 2025

作者： Fathi, Hamid Skorikov, Alexander van Leeuwen, Tristan Science Park 123 Amsterdam1098 XG Netherlands Mathematical Institute Utrecht University Budapestlaan 6 Utrecht3584 CD Netherlands

ISBN: (纸本)9783031923685

Total Variation (TV) regularized reconstruction is one of the most relevant methods to improve the quality of limited-angle tomographic reconstructions. Nevertheless, the accuracy of computed tomography (CT) reconstructions with a limited number of measurements can be further improved by selecting the most informative acquisition angles. This optimal experimental design (OED) task can be formulated as a bi-level optimization problem, with selecting optimal angle combinations (experimental design parameter) on the upper-level and tomographic reconstruction on the lower-level. However, integrating TV regularized reconstruction into the bi-level optimization approach is non-trivial because of the large number of iterations required for the algorithm convergence, which impedes naive computation of gradients of the upper-level objective with respect to the experimental design parameter. In this work, we address this problem by employing implicit differentiation approach to calculate the upper-level objective gradient. Moreover, we utilize inexact methods to dynamically adjust the accuracy of the lower-level solver, refining the gradient calculation as needed. We demonstrate that this approach makes OED with TV regularized reconstruction applicable to realistic 3D data. Our numerical results demonstrate that the angles selected by our bi-level optimization framework significantly outperform the standard equidistant angular selection. The proposed approach is therefore effective in minimizing experimental time and radiation dose requirements for CT reconstruction of objects benefiting from TV regularization, and can be readily extended to other types of computationally demanding iterative reconstruction algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gradient methods

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Residual Whiteness Principle for Automatic Parameter Selection in 2 - 2 Image Super-Resolution Problems 8th

Residual Whiteness Principle for Automatic Parameter Selecti...

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8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Pragliola, Monica Calatroni, Luca Lanza, Alessandro Sgallari, Fiorella CNRS UCA INRIA Morpheme I3S Sophia-Antipolis France Department of Mathematics University of Bologna Bologna Italy

ISBN: (纸本)9783030755485

We propose an automatic parameter selection strategy for variational image super-resolution of blurred and down-sampled images corrupted by additive white Gaussian noise (AWGN) with unknown standard deviation. By exploiting particular properties of the operators describing the problem in the frequency domain, our strategy selects the optimal parameter as the one optimising a suitable residual whiteness measure. Numerical tests show the effectiveness of the proposed strategy for generalised 2 - 2 Tikhonov problems. © 2021, Springer Nature Switzerland AG.

关键词： White noise

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DeepAttent: Saliency Prediction with Deep Multi-scale Residual Network 3rd

DeepAttent: Saliency Prediction with Deep Multi-scale Residu...

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3rd international conference on computer vision and Image Processing (CVIP)

作者： Dwivedi, Kshitij Singh, Nitin Shanmugham, Sabari R. Kumar, Manoj Singapore Univ Technol & Design Singapore Singapore Samsung Res Inst Bengaluru India DataRobot Singapore Singapore

ISBN: (纸本)9789813292918;9789813292901

Predicting where humans look in a given scene is a well-known problem with multiple applications in consumer cameras, human-computer interaction, robotics, and gaming. With large-scale image datasets available for human fixation, it is now possible to train deep neural networks for generating a fixationmap. Human fixations are a function of both local visual features and global context. We incorporate this in a deep neural network by using global and local features of an image to predict human fixations. We sample multi-scale features of the deep residual network and introduce a new method for incorporating these multi-scale features for the end-to-end training of our network. Our model DeepAttent obtains competitive results on SALICON and iSUN datasets and outperforms state-of-the-art methods on various metrics.

关键词： Saliency Neural networks Multi-scale

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