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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data III"

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International conference on Aerospace System Science and Engineering, ICASSE 2019

International Conference on Aerospace System Science and Eng...

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International conference on Aerospace System Science and Engineering, ICASSE 2019

ISBN: (纸本)9789811517723

The proceedings contain 29 papers. The special focus in this conference is on Aerospace System Science and Engineering. The topics include: New Model and Analytical Review of Approaches to Buckling Problem Investigation of Structurally Anisotropic Aircraft Panels Made from Composite Materials;acoustic Fatigue Research for Honeycomb Sandwich Structure with Impact Damage Based on Vibro-Acoustic Coupling Analysis;combining Autoencoder with Similarity Measurement for Aircraft Engine Remaining Useful Life Estimation;development of Ryerson’s Hyperloop Pod Systems Using a Modular Approach;revisited: Machine Intelligence in Heterogeneous Multi-Agent Systems;dynamics of Partial Space Elevator with Parallel Tethers and Multiple Climbers;design and Stress Analysis for Aircraft Structure Repair Beyond Specification;Ultra-Rapid Direct Satellite Selection Algorithm for Multi-GNSS;concepts for Morphing Airfoil Using Novel Auxetic Lattices;an Improved Deep Convolutional Neural Network to Predict Airfoil Lift Coefficient;remote Sensing image Change Detection and Location Based on Dynamic Level Set Model;virtual Simulation Experiment System for Spacecraft Orbital Principle and Its Teaching Application;Verification of GPS iii and QZSS L1C Signal Joint Positioning Performance with Software-Defined Receiver;study on Impact Behavior of Composite Sandwich Structure with Different Interlayer Angles;Positioning and Timing Test Campaign Based on China Area Positioning System (CAPS);data-Augmented Design of Turbulence Modeling;spacecraft Anomaly Detection via Transformer reconstruction Error;Altitude Ignition by Reducing IDT at Low Pressure with Additive;On Ground Test of an IMU/GNSS Receiver for Atmospheric Re-Entry Vehicle Applications;stall Margin Enhancement of Aeroengine Compressor with a Novel Type of Alternately Swept Blades.

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Isotropic MRI Super-Resolution reconstruction with Multi-scale Gradient Field Prior 1

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Sui, Yao Afacan, Onur Gholipour, Ali Warfield, Simon K. Harvard Med Sch Boston MA 02115 USA Boston Childrens Hosp Boston MA 02115 USA

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

In this work, we proposed a novel image-based MRI super-resolution reconstruction (SRR) approach based on anisotropic acquisition schemes. We achieved superior reconstruction to state-of-the-art work by introducing a new multi-scale gradient field prior that guides the reconstruction of the high-resolution (HR) image. The prior improves both spatial smoothness and edge preservation. The inverse of the forward model of image formation is used to propagate the gradient guidance from the low-resolution (LR) images to the HR image space. The gradient fields over multiple scales were exploited for more accurate edge localization in the reconstruction. The proposed SRR allows inter-volume motion during the MRI scans and can incorporate with the LR images with arbitrary orientations and displacements in the frequency space, such as orthogonal and origin-shifted scans. The proposed approach was evaluated on the synthetic data as well as the data acquired on a Siemens 3T MRI scanner containing 45 MRI scans from 14 subjects. The evaluation results demonstrate that our proposed prior leads to improved SRR as compared to state-of-the-art priors, and that the proposed SRR obtains better results at lower or the same cost in scan time than direct HR acquisition. In particular, the anatomical structures of hippocampus can be clearly shown in our reconstructed images. This is a significant improvement for the in vivo studies of the hippocampus.

关键词： Magnetic resonance imaging image reconstruction Super-resolution Multi-scale gradient field Total variance

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Limited-Angle Computed Tomography reconstruction using Combined FDK-Based Neural Network and U-Net

Limited-Angle Computed Tomography Reconstruction using Combi...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yiying Wang Tao Yang Weimin Huang Department of Computer Science University College London Institute for Infocomm Research A*STAR Singapore

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The limited-angle cone-beam Computed Tomography (CT) is often used in C-arm for clinical diagnosis with the advantages of cheap cost and radiation dose reduction. However, due to incomplete projection data, the 3-dimensional CT images reconstructed by conventional methods, such as the Feldkamp, Davis and Kres (FDK) algorithm [1], suffer from heavy artifacts and missing features. In this paper, we propose a novel pipeline of neural networks jointly by a FDK-based neural network revisited from Würfl et al.'s work [2] and an image domain U-Net to enhance the 3-dimensional reconstruction quality for limited projection sinogram less than 180 degrees, i.e. 145 degrees in our work. Experimental results, on simulated projections of real-scan CTs, show that the proposed pipeline can reduce some of the major artifacts caused by the limited views while keep the key features, with a 16.60% improvement than Würfl et al.'s work on peak signal-to-noise ratio.

关键词： Neural networks Computed tomography image reconstruction Training Pipelines Machine learning Measurement

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SCFusion: Real-time Incremental Scene reconstruction with Semantic Completion

SCFusion: Real-time Incremental Scene Reconstruction with Se...

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International conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Shun-Cheng Wu Keisuke Tateno Nassir Navab Federico Tombari Technische Universitat München Germany Google Inc. Switzerland

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Real-time scene reconstruction from depth data inevitably suffers from occlusion, thus leading to incomplete 3D models. Partial reconstructions, in turn, limit the performance of algorithms that leverage them for applications in the context of, e.g., augmented reality, robotic navigation, and 3D mapping. Most methods address this issue by predicting the missing geometry as an offline optimization, thus being incompatible with real-time applications. We propose a framework that ameliorates this issue by performing scene reconstruction and semantic scene completion jointly in an incremental and real-time manner, based on an input sequence of depth maps. Our framework relies on a novel neural architecture designed to process occupancy maps and leverages voxel states to accurately and efficiently fuse semantic completion with the 3D global model. We evaluate the proposed approach quantitatively and qualitatively, demonstrating that our method can obtain accurate 3D semantic scene completion in real-time.

关键词： Three-dimensional displays Semantics image reconstruction Real-time systems Pipelines Shape Solid modeling

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Compression and Completion of Animated Point Clouds using Topological Properties of the Manifold

Compression and Completion of Animated Point Clouds using To...

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International conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Linfei Pan Ľubor Ladický Marc Pollefeys ETH Zurich ETH Zurich/Microsoft

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Recent progress in consumer hardware allowed for the collection of a large amount of animated point cloud data, which is on the one hand highly redundant and on the other hand incomplete. Our goal is to bridge this gap and find a low dimensional representation capable of approximation to a desired precision and completion of missing data. Model-less non-rigid 3D reconstruction algorithms, formulated as a linear factorization of observed point tracks into static shape component and dynamic pose, have been found insufficient to create suitable generative models, capable of generating new unobserved poses. This is due to the non-locality of the linear models, over-fitting to the non-causal correlations present in the data, which manifests in the reconstruction containing rigidly behaving not directly connected parts. In this paper, we propose a new method that can distinguish body parts and factorize the data into shape and pose purely using topological properties of the manifold-local deformations and neighborhoods. To obtain localized factorization, we formulate the deformation distance between two point tracks as the smallest deformation along the path between them. After embedding such distance in low dimensional space, a clustering of embedded data leads to close to rigid components, suitable as initialization for fitting a model-a skinned rigged mesh, used extensively in computer graphics. As both local deformations and neighborhoods of a point are local and can be estimated only from the part of the animation, the method can be used to recover unobserved data in each frame.

关键词： Strain Three-dimensional displays data models Computational modeling Shape Animation image reconstruction

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Implicit Functions in Feature Space for 3D Shape reconstruction and Completion

Implicit Functions in Feature Space for 3D Shape Reconstruct...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Julian Chibane Thiemo Alldieck Gerard Pons-Moll Max Planck Institute for Informatics Saarland Informatics Campus Germany University of Würzburg Germany Computer Graphics Lab TU Braunschweig Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete. Processing of such 3D inputs is an increasingly important problem as they are the output of 3D scanners, which are becoming more accessible, and are the intermediate output of 3D computer vision algorithms. Recently, learned implicit functions have shown great promise as they produce continuous reconstructions. However, we identified two limitations in reconstruction from 3D inputs: 1) details present in the input data are not retained, and 2) poor reconstruction of articulated humans. To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans. Our work differs from prior work in two crucial aspects. First, instead of using a single vector to encode a 3D shape, we extract a learnable 3-dimensional multi-scale tensor of deep features, which is aligned with the original Euclidean space embedding the shape. Second, instead of classifying x-y-z point coordinates directly, we classify deep features extracted from the tensor at a continuous query point. We show that this forces our model to make decisions based on global and local shape structure, as opposed to point coordinates, which are arbitrary under Euclidean transformations. Experiments demonstrate that IF-Nets outperform prior work in 3D object reconstruction in ShapeNet, and obtain significantly more accurate 3D human reconstructions. Code and project website is available at https://***/ifnets/.

关键词： Three-dimensional displays Shape image reconstruction Topology Feature extraction Computer vision Tensile stress

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Disease-image Specific Generative Adversarial Network for Brain Disease Diagnosis with incomplete Multi-modal Neuroimages 1

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Pan, Yongsheng Liu, Mingxia Lian, Chunfeng Xia, Yong Shen, Dinggang Northwestern Polytech Univ Sch Comp Sci & Engn Xian 710072 Peoples R China Univ North Carolina Dept Radiol Chapel Hill NC 27599 USA Univ North Carolina BRIC Chapel Hill NC 27599 USA

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

incomplete data problem is unavoidable in automated brain disease diagnosis using multi-modal neuroimages (e.g., MRI and PET). To utilize all available subjects to train diagnostic models, deep networks have been proposed to directly impute missing neuroimages by treating all voxels in a 3D volume equally. These methods are not diagnosis-oriented, as they ignore the disease-image specific information conveyed in multi-modal neuroimages, i.e., (1) disease may cause abnormalities only at local brain regions, and (2) different modalities may highlight different disease-associated regions. In this paper, we propose a unified disease-image specific deep learning framework for joint image synthesis and disease diagnosis using incomplete multi-modal neuroimaging data. Specifically, by using the whole-brain images as input, we design a disease-image specific neural network (DSNN) to implicitly model disease-image specificity in MRI/PET scans using the spatial cosine kernel. Moreover, we develop a feature-consistent generative adversarial network (FGAN) to synthesize missing images, encouraging DSNN feature maps of synthetic images and their respective real images to be consistent. Our DSNN and FGAN can be jointly trained, by which missing images are imputed in a task-oriented manner for brain disease diagnosis. Experimental results on 1, 466 subjects suggest that our method not only generates reasonable neuroimages, but also achieves the state-of-the-art performance in both tasks of Alzheimer's disease (AD) identification and mild cognitive impairment (MCI) conversion prediction.

关键词： Generative adversarial networks

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Model Assessment Through data Assimilation of Realistic data in Cardiac Electrophysiology 10th

Model Assessment Through Data Assimilation of Realistic Data...

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10th International conference on Functional Imaging and Modeling of the Heart (FIMH)

作者： Gerard, Antoine Collin, Annabelle Bureau, Gautier Moireau, Philippe Coudiere, Yves Electrophysiol & Heart Modeling Inst IHU Liryc F-33000 Pessac France Univ Bordeaux IMB UMR 5251 Talence France Bordeaux INP IMB UMR 5251 Talence France INRIA Inria Bordeaux Sud Ouest Talence France Univ Paris Saclay INRIA Palaiseau France Ecole Polytech CNRS UMR 7649 Inst Polytech Paris LMS Palaiseau France

ISBN: (纸本)9783030219499;9783030219482

We consider a model-based estimation procedure - namely a data assimilation algorithm - of the atrial depolarization state of a subject using data corresponding to electro-anatomical maps. Our objective is to evaluate the sensitivity of such a model-based reconstruction with respect to model choices. The followed data assimilation approach is capable of using electrical activation times to adapt a monodomain model simulation, thanks to an ingenious model-data fitting term inspired from image processing. The resulting simulation smoothes and completes the activation maps when they are spatially incomplete. Moreover, conductivity parameters can also be inferred. The model sensitivity assessment is performed based on synthetic data generated with a validated realistic atria model and then inverted using simpler modeling ingredients. In particular, the impact of the muscle fibers definition and corresponding anisotropic conductivity parameters is studied. Finally, an application of the method to real data is presented, showing promising results.

关键词： Cardiac modeling Electrophysiology data assimilation

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Adversarial Feature Disentanglement for Place Recognition Across Changing Appearance

Adversarial Feature Disentanglement for Place Recognition Ac...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Li Tang Yue Wang Qianhui Luo Xiaqing Ding Rong Xiong State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou P. R. China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

When robots move autonomously for long-term, varied appearance such as the transition from day to night and seasonal variation brings challenges to visual place recognition. Defining an appearance condition (e.g. a season, a kind of weather) as a domain, we consider that the desired representation for place recognition (i) should be domain-unrelated so that images from different time can be matched regardless of varied appearance, (ii) should be learned in a self-supervised manner without the need of massive manually labeled data, and (iii) should be able to train among multiple domains in one model to keep limited model complexity. This paper sets to find domain-unrelated features across extremely changing appearance, which can be used as image descriptors to match between images collected at different conditions. We propose to use the adversarial network to disentangle domain-unrelated and domain-related features, which are named place and appearance features respectively. During training, only domain information is needed without requiring manually aligned image sequences. Experiments demonstrated that our method can disentangle place and appearance features in both toy case and images from the real world, and the place feature is qualified in place recognition tasks under different appearance conditions. The proposed network is also adaptable to multiple domains without increasing model capacity and shows favorable generalization.

关键词： Feature extraction Training image reconstruction image recognition Robustness Machine learning Neural networks

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Recovery of Subspace Structure from High-Rank data with Missing Entries

Recovery of Subspace Structure from High-Rank Data with Miss...

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IEEE International conference on image Processing

作者： João Carvalho Manuel Marques João P. Costeira Univ. Lisboa Lisbon Portugal

We propose a method to reconstruct and cluster incomplete high-dimensional data lying in a union of low-dimensional subspaces. Exploring the sparse representation model, we jointly estimate the missing data while imposing the intrinsic subspace structure. Although we have a non-convex problem, we propose an algorithm robust to initialization. Extensive experiments with synthetic and real data show that our approach leads to significant improvements in the reconstruction and segmentation, outperforming current state of the art for both low and high-rank data.

关键词： Clustering algorithms Sparse matrices image reconstruction data models Motion segmentation Computer vision Signal processing algorithms

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