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

共 14809 条记录，以下是551-560 订阅

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GIM: LEARNING GENERALIZABLE image MATCHER from INTERNET VIDEOS 12

GIM: LEARNING GENERALIZABLE IMAGE MATCHER FROM INTERNET VIDE...

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12th International conference on Learning Representations, ICLR 2024

作者： Shen, Xuelun Cai, Zhipeng Yin, Wei Müller, Matthias Li, Zijun Wang, Kaixuan Chen, Xiaozhi Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University 361005 China Intel Labs United States DJI Technology China

image matching is a fundamental computer vision problem. While learning-based methods achieve state-of-the-art performance on existing benchmarks, they generalize poorly to in-the-wild images. Such methods typically need to train separate models for different scene types (e.g., indoor vs. outdoor) and are impractical when the scene type is unknown in advance. One of the underlying problems is the limited scalability of existing data construction pipelines, which limits the diversity of standard image matching datasets. To address this problem, we propose GIM, a self-training framework for learning a single generalizable model based on any image matching architecture using internet videos, an abundant and diverse data source. Given an architecture, GIM first trains it on standard domain-specific datasets and then combines it with complementary matching methods to create dense labels on nearby frames of novel videos. These labels are filtered by robust fitting, and then enhanced by propagating them to distant frames. The final model is trained on propagated data with strong augmentations. Not relying on complex 3D reconstruction makes GIM much more efficient and less likely to fail than standard SfM-and-MVS based frameworks. We also propose ZEB, the first zero-shot evaluation benchmark for image matching. By mixing data from diverse domains, ZEB can thoroughly assess the cross-domain generalization performance of different methods. Experiments demonstrate the effectiveness and generality of GIM. Applying GIM consistently improves the zero-shot performance of 3 state-of-the-art image matching architectures as the number of downloaded videos increases (Fig. 1 (a));with 50 hours of YouTube videos, the relative zero-shot performance improves by 6.9% - 18.1%. GIM also enables generalization to extreme cross-domain data such as Bird Eye View (BEV) images of projected 3D point clouds (Fig. 1 (c)). More importantly, our single zero-shot model consistently outperforms domain-s

关键词： Benchmarking

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Revolutionising image Enhancement Leveraging Power OF CNN'S 3

Revolutionising Image Enhancement Leveraging Power OF CNN'S

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3rd International conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Vijaya, J. Gopu, Arunkumar Suman, Pediredla Chaitanya, Sambangi DSA I IIIT Naya Raipur Raipur India CSE Dayananda Sagar University Banglore India Data Science Artificial Intelligence IIIT Naya Raipur Raipur India Computer Science Engineering IIIT Naya Raipur Raipur India

ISBN: (纸本)9798350372120

image restoration has several uses, remote sensing, surveillance, medical imaging, and computational photography. Recovering high-quality image content from lower-quality images is its goal. Convolutional neural networks (CNN's) have shown better results than the existing methods for image restoration in amazing ways. Many of the present CNN-based methods work with either fully-resolution or lower-resolution representations. While the second scenario produces acceptably trustworthy but spatially less accurate outputs, the first instance gives spatially the same but contextually less accurate results, image restoration has several uses, in the medical field, traffic policies and criminal investigation, and astronomical image enhancement Its purpose is to recover greater quality image content from its old version, which has less quality So, having the overall aim of maintaining spatially intact higher quality representations over the whole network and gaining the maximum contextual information from the previous lower resolution representations. In previous projects, they used RNN and CNN to get the output we are using CNN and aim for better output by doing the project in our kind of way. Our project's crucial component is a multi-scale residual block, which is made up of several vital components, including (a)exchange of information between the multiresolution streams, (b) channel and special attention mechanisms for contextual information capturing (c) parallel multi-resolution convolution streams for the extracting multi-scaled features,(d) attention multi-scale feature aggregation. In brief, our project will aim towards learning a bunch of features that will maintain higher resolution spatial information and details while combining contextual data from various scales. Thereby increasing the image quality to a greater version resolution, physical constraints, low dynamic range, downsampling, spatial features, and receptive area. © 2024 IEEE.

关键词： image reconstruction

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DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume reconstruction 26th

DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volu...

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26th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： He, Xiaoxiao Tan, Chaowei Han, Ligong Liu, Bo Axel, Leon Li, Kang Metaxas, Dimitris N. Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA FocusAI Inc Sunnyvale CA USA Walmart Global Tech San Bruno CA USA NYU Sch Med New York NY USA Sichuan Univ West China Biomed Big Data Ctr West China Hosp Chengdu Peoples R China

ISBN: (纸本)9783031439896;9783031439902

Accurate 3D cardiac reconstruction from cine magnetic resonance imaging (cMRI) is crucial for improved cardiovascular disease diagnosis and understanding of the heart's motion. However, current cardiac MRI-based reconstruction technology used in clinical settings is 2D with limited through-plane resolution, resulting in low-quality reconstructed cardiac volumes. To better reconstruct 3D cardiac volumes from sparse 2D image stacks, we propose a morphology-guided diffusion model for 3D cardiac volume reconstruction, DMCVR, that synthesizes high-resolution 2D images and corresponding 3D reconstructed volumes. Our method outperforms previous approaches by conditioning the cardiac morphology on the generative model, eliminating the time-consuming iterative optimization process of the latent code, and improving generation quality. The learned latent spaces provide global semantics, local cardiac morphology and details of each 2D cMRI slice with highly interpretable value to reconstruct 3D cardiac shape. Our experiments show that DMCVR is highly effective in several aspects, such as 2D generation and 3D reconstruction performance. With DMCVR, we can produce high-resolution 3D cardiac MRI reconstructions, surpassing current techniques. Our proposed framework has great potential for improving the accuracy of cardiac disease diagnosis and treatment planning. Code can be accessed at https://***/hexiaoxiao- cs/DMCVR.

关键词： Diffusion model 3D reconstruction Generative model

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9th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2024, held in conjunction with the 27th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2024

9th International Workshop on Simulation and Synthesis in Me...

引用

9th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2024, held in conjunction with the 27th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031732805

The proceedings contain 19 papers. The special focus in this conference is on Simulation and Synthesis in Medical Imaging. The topics include: Adapted nnU-Net: A Robust Baseline for Cross-Modality Synthesis and Medical image Inpainting;Beyond MR image Harmonization: Resolution Matters Too;benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted data;a Dual-Task Mutual Learning Framework for Predicting Post-thrombectomy Cerebral Hemorrhage;TSynD: Targeted Synthetic data Generation for Enhanced Medical image Classification: Leveraging Epistemic Uncertainty to Improve Model Performance;beyond Intensity Transforms: Medical image Synthesis Under Large Deformation;sim2Real in Endoscopy Segmentation with a Novel Structure Aware image Translation;fireflies: Photorealistic Simulation and Optimization of Structured Light Endoscopy;exogenous Agent-Free Synthetic Post-contrast Imaging with a Cascade of Deep Networks for Enhancement Prediction After Tumor Resection. A Parametric-Map Oriented Approach;OCT Scans Simulation Framework for data Augmentation and Controlled Evaluation of Signal Processing Approaches;enhancing Quantitative image Synthesis Through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-Ray image;latent Pollution Model: The Hidden Carbon Footprint in 3D image Synthesis;Synthesizing Scalable CFD-Enhanced Aortic 4D Flow MRI for Assessing Accuracy and Precision of Deep-Learning image reconstruction and Segmentation Tasks;MedEdit: Counterfactual Diffusion-Based image Editing on Brain MRI;Using MR Physics for Domain Generalisation and Super-Resolution;Single-Scan mpMRI Calibration of Multi-species Brain Tumor Dynamics with Mass Effect;annotated Biomedical Video Generation Using Denoising Diffusion Probabilistic Models and Flow Fields.

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PlankAssembly: Robust 3D reconstruction from Three Orthographic Views with Learnt Shape Programs

PlankAssembly: Robust 3D Reconstruction from Three Orthograp...

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IEEE/CVF International conference on Computer Vision (ICCV)

作者： Hu, Wentao Zheng, Jia Zhang, Zixin Yuan, Xiaojun Yin, Jian Zhou, Zihan Sun Yat Sen Univ Guangzhou Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou Peoples R China Manycore Tech Inc Hangzhou Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798350307184

In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models. Existing methods for this problem reconstruct 3D models by back-projecting the 2D observations into 3D space while maintaining explicit correspondence between the input and output. Such methods are sensitive to errors and noises in the input, thus often fail in practice where the input drawings created by human designers are imperfect. To overcome this difficulty, we leverage the attention mechanism in a Transformer-based sequence generation model to learn flexible mappings between the input and output. Further, we design shape programs which are suitable for generating the objects of interest to boost the reconstruction accuracy and facilitate CAD modeling applications. Experiments on a new benchmark dataset show that our method significantly outperforms existing ones when the inputs are noisy or incomplete.

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A Robust EM Algorithm for Radio Interferometric Imaging in The Presence of Outliers 36

A Robust EM Algorithm for Radio Interferometric Imaging in T...

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36th IEEE Workshop on Signal Processing Systems (SiPS)

作者： Mhiri, Yassine El Korso, Mohammed Nabil Breloy, Arnaud Larzabal, Pascal Univ Paris Saclay SATIE Gif Sur Yvette France Univ Paris Saclay L2S Gif Sur Yvette France Univ Paris Nanterre LEME Ville Davray France

ISBN: (数字)9781665485241

ISBN: (纸本)9781665485241

image synthesis in the context of radio interferometric data can be expressed as a signal reconstruction from incomplete Fourier measurements. Most imaging techniques for radio interferometry lie in minimizing the least square error between the reconstructed image and the observed data assuming an additive white gaussian noise. In this paper, we derive an expectation-maximization based imaging algorithm that handles the presence of outliers in the observed data. Subsequently, we propose a new generic image synthesis algorithm based on the expectation-maximization algorithm, leading to a computationally efficient method.

关键词： Radioastronomy expectation-maximization image restauration

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Tomographic image reconstruction Method Using MapReduce in Big data Environment 16

Tomographic Image Reconstruction Method Using MapReduce in B...

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16th IEEE International conference on Semantic Computing (ICSC)

作者： Alves, Gabriel M. Cruvinel, Paulo E. Fed Univ Sao Carlos UFSCar Postgrad Program Comp Sci Sao Carlos SP Brazil Embrapa Instrumentat Sao Carlos SP Brazil Fed Inst Educ Sci & Technol Campus Sao Joao da Boa Vista Sao Joao Da Boa Vista Brazil

ISBN: (纸本)9781665434188

In this study, we propose a new tomographic reconstruction method for high-resolution samples based on the MapReduce model. We executed the method in a big data environment with a cluster installed on the Amazon Web Services (AWS) platform. The big data environment framework considered four sets of matrices from a single heterogeneous plexiglass phantom sample, totaling 7,840 matrices (35.63 GB) processed by 12 different frameworks and producing 427.56 GB of processed tomographic data. The proposed method enabled the analysis of large numbers of agricultural samples using X-ray tomography to support management based on precision agriculture paradigms, the decision-making processes of which require an increasing number of analyses. Index Terms-tomographic

关键词： tomographic image reconstruction MapReduce big data computed tomography

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4th Workshop on Deep Generative Models for Medical image Computing and Computer Assisted Intervention, DGM4MICCAI 2024, held in Conjunction with 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

4th Workshop on Deep Generative Models for Medical Image Com...

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4th Workshop on Deep Generative Models for Medical image Computing and Computer Assisted Intervention, DGM4MICCAI 2024, held in Conjunction with 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031727436

The proceedings contain 21 papers. The special focus in this conference is on Deep Generative Models for Medical image Computing and Computer Assisted Intervention. The topics include: Energy-Based Prior Latent Space Diffusion Model for reconstruction of Lumbar Vertebrae from Thick Slice MRI;anatomically-Guided Inpainting for Local Synthesis of Normal Chest Radiographs;enhancing Cross-Modal Medical image Segmentation Through Compositionality;unpaired Modality Translation for Pseudo Labeling of Histology images;SNAFusion: Distilling 2D Axial Plane Diffusion Priors for Sparse-View 3D Cone-Beam CT Imaging;SynthBrainGrow: Synthetic Diffusion Brain Aging for Longitudinal MRI data Generation in Young People;denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting;panoptic Segmentation of Mammograms with Text-to-image Diffusion Model;interactive Generation of Laparoscopic Videos with Diffusion Models;Multi-parametric MRI to FMISO PET Synthesis for Hypoxia Prediction in Brain Tumors;qMRI Diffuser: Quantitative T1 Mapping of the Brain Using a Denoising Diffusion Probabilistic Model;on Differentially Private 3D Medical image Synthesis with Controllable Latent Diffusion Models;five Pitfalls When Assessing Synthetic Medical images with Reference Metrics;Augmenting Prostate MRI dataset with Synthetic Volumetric images from Zone-Conditioned Diffusion Generative Model;tiBiX: Leveraging Temporal Information for Bidirectional X-Ray and Report Generation;Segmentation-Guided MRI reconstruction for Meaningfully Diverse reconstructions;Non-reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs;latentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework;How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models.

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Spectral Feature Augmentation for Graph Contrastive Learning and Beyond 37

Spectral Feature Augmentation for Graph Contrastive Learning...

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37th AAAI conference on Artificial Intelligence (AAAI) / 35th conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Zhang, Yifei Zhu, Hao Song, Zixing Koniusz, Piotr King, Irwin Chinese Univ Hong Kong Hong Kong Peoples R China Australian Natl Univ Canberra ACT Australia CSIRO Data61 Canberra ACT Australia

ISBN: (纸本)9781577358800

Although augmentations (e.g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, complementary yet not well researched strategy. Thus, we present a novel spectral feature argumentation for contrastive learning on graphs (and images). To this end, for each data view, we estimate a low-rank approximation per feature map and subtract that approximation from the map to obtain its complement. This is achieved by the proposed herein incomplete power iteration, a non-standard power iteration regime which enjoys two valuable byproducts (under mere one or two iterations): (i) it partially balances spectrum of the feature map, and (ii) it injects the noise into rebalanced singular values of the feature map (spectral augmentation). For two views, we align these rebalanced feature maps as such an improved alignment step can focus more on less dominant singular values of matrices of both views, whereas the spectral augmentation does not affect the spectral angle alignment (singular vectors are not perturbed). We derive the analytical form for: (i) the incomplete power iteration to capture its spectrum-balancing effect, and (ii) the variance of singular values augmented implicitly by the noise. We also show that the spectral augmentation improves the generalization bound. Experiments on graph/image datasets show that our spectral feature augmentation outperforms baselines, and is complementary with other augmentation strategies and compatible with various contrastive losses.

关键词： Approximation theory

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image Based reconstruction of Liquids from 2D Surface Detections

Image Based Reconstruction of Liquids from 2D Surface Detect...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Richter, Florian Orosco, Ryan K. Yip, Michael C. Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we present a solution to the challenging problem of reconstructing liquids from image data. The challenges in reconstructing liquids, which is not faced in previous reconstruction works on rigid and deforming surfaces, lies in the inability to use depth sensing and color features due the variable index of refraction, opacity, and environmental reflections. Therefore, we limit ourselves to only surface detections (i.e. binary mask) of liquids as observations and do not assume any prior knowledge on the liquids properties. A novel optimization problem is posed which reconstructs the liquid as particles by minimizing the error between a rendered surface from the particles and the surface detections while satisfying liquid constraints. Our solvers to this optimization problem are presented and no training data is required to apply them. We also propose a dynamic prediction to seed the reconstruction optimization from the previous time-step. We test our proposed methods in simulation and on two new liquid datasets which we open source1 so the broader research community can continue developing in this under explored area.

关键词： Surface reconstruction Liquids Three-dimensional displays Training data Refractive index Reflection Sensors

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