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

共 14811 条记录，以下是581-590 订阅

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FFT-BASED FILTERING APPROACH TO FUSE PHOTOGRAMMETRY AND PHOTOMETRIC STEREO 3D data 6

FFT-BASED FILTERING APPROACH TO FUSE PHOTOGRAMMETRY AND PHOT...

引用

ISPRS GeoSpatial conference / Joint 6th Sensors and Models in Photogrammetry and Remote Sensing conference (SMPR) / 4th Geospatial Information Research (GIResearch) conference

作者： Karami, A. Varshosaz, M. Menna, F. Remondino, F. Luhmann, T. Bruno Kessler Fdn FBK 3D Opt Metrol 3DOM Unit Trento Italy Univ Trento Trento Italy KN Toosi Univ Technol Geomat Engn Fac Tehran Iran Jade Univ Appl Sci Inst Appl Photogrammetry & Geoinformat Oldenburg Germany

image-based 3D reconstruction has been successfully employed for micro-measurements and industrial quality control purposes. However, obtaining a highly-detailed and reliable 3D reconstruction and inspection of non-collaborative surfaces is still an open issue. Photometric stereo (PS) offers the high spatial frequencies of the surface, but the low frequency is erroneous due to the mathematical model's assumptions and simplifications on how light interacts with the object surface. Photogrammetry, on the other hand, gives precise low-frequency information but fails to utilize high frequencies. As a result, in this research, we present a fusion strategy in Fourier domain to replace the low spatial frequencies of PS with the corresponding photogrammetric frequencies in order to have correct low frequencies while maintaining high frequencies from PS. The proposed method was tested on three different objects. Different cloud-to-cloud comparisons were provided between reference data and the 3D points derived from the proposed method to evaluate high and low frequency information. The obtained 3D findings demonstrated how the proposed methodology generates a high-detail 3D reconstruction of the surface topography (below 20 mu m) while maintaining low-frequency information (0.09 mu m on average for three different testingobjects) by fusingphotogrammetric and PSdepth datawith the proposed FFT-based method.

关键词： Photogrammetry Photometric stereo High-resolution 3D reconstruction Fast Fourier Transform (FFT) Fusion

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Clothed Human Model Estimation from Unseen Partial Point Clouds with Meta-learning 1st

Clothed Human Model Estimation from Unseen Partial Point Cl...

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1st International Artificial Intelligence conference, IAIC 2023

作者： Fang, Chenghao Wang, Kangkan Yin, Shihao Li, Shaoyuan Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9789819712793

This work aims to address the problems of clothed human reconstruction from unseen partial point clouds. Existing methods focus on estimating vertex offsets on top of parametric models for clothing details but with the limitation of the fixed topology, or reconstructing non-parametric shapes using implicit functions which are lack of semantic information. Moreover, due to limited training data, large variations in clothing details, and domain gaps between training data and real data, these methods often have poor generalization ability on real data. In this paper, we propose a generalizable approach for estimating dressed human models from single-frame partial point clouds based on meta-learning. Specifically, we first learn a meta-model that can efficiently estimate the parameters of unclothed human models from unseen data by a fast fine-tuning. Based on the unclothed human models, we further meta-learn the point-based human models in clothing with local geometric features, which is topologically flexible and rich in human details. Our approach outperforms previous work in terms of reconstruction accuracy, as evidenced by qualitative and quantitative results obtained from a variety of datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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Lithium-Ion Battery Health Estimation with incomplete Charging data and Spiking ResNet 15

Lithium-Ion Battery Health Estimation with Incomplete Chargi...

引用

15th IEEE Global Reliability and Prognostics and Health Management conference, PHM-Beijing 2024

作者： Wang, Mingyang Li, Naipeng Lei, Yaguo Li, Xiang Yang, Bin Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350354010

The accurate estimation of state of health (SOH) plays a crucial role in ensuring the safe operation of batteries. Most existing SOH estimation methods are based on complete charging or discharging data. However, in real-world applications, data are often derived from partial charging or discharging processes influenced by varying user habits, which brings great challenges to the accurate SOH estimation of batteries. In this article, a SOH estimation method based on incremental capacity (IC) curve reconstruction and spiking ResNet is proposed to deal with the SOH estimation issue using partial data. The monitoring data are preprocessed using differentiation and Gaussian filtering to obtain the IC curves. A nonparametric degradation model is constructed using functional principal component analysis (FPCA). Subsequently, the functional principal component (FPC) scores are estimated by the maximum likelihood estimation (PAMLE) algorithm to reconstruct the IC curves. Ultimately, the SOH can be estimated through the spiking ResNet. The proposed method is evaluated using the XJTU battery experimental data. The results demonstrate that the proposed method provides more accurate and reliable estimation results than other methods. © 2024 IEEE.

关键词： State of charge

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City-scale Ground Segmentation of Aerial LiDAR by image-analogous Gradients 15

City-scale Ground Segmentation of Aerial LiDAR by Image-anal...

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15th International conference on Machine Vision (ICMV)

作者： Azam, Muhammad Jacoby, Derek Coady, Yvonne Univ Victoria Victoria BC Canada

ISBN: (纸本)9781510666184;9781510666191

LiDAR Point Cloud segmentation is a key input to downstream tasks such as object recognition and classification, obstacle avoidance, and even 3D reconstruction. Akey challenge in the segmentation of large city-scale datasets is uneven distribution of points to specific classes and significant class imbalances. As highly detailed point cloud datasets of urban environments become available, neural networks have shown significant performance in recognizing large well-defined objects. However, data is fed into these networks in chunks and the scheme by which data is presented for training and evaluation can have a significant impact on performance. In this work, we establish a method analogous to gradients in image processing to segment the ground in point clouds, achieving an accuracy of 91.4% on the Sensaturban dataset. By isolating the ground, we reduce the quantity of classes that need to be segmented from structures in urban LiDAR and improve data partitioning schemes when combined with random/grid down-sampling techniques for neural network inputs.

关键词： LiDAR segmentation urban point cloud neural networks

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Back to the Future: Challenges of Sparse and Irregular Medical image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David Stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Test-Time Dynamic image Fusion 38

Test-Time Dynamic Image Fusion

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Cao, Bing Xia, Yinan Ding, Yi Zhang, Changqing Hu, Qinghua College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Lab of Machine Learning Tianjin China

The inherent challenge of image fusion lies in capturing the correlation of multi-source images and comprehensively integrating effective information from different sources. Most existing techniques fail to perform dynamic image fusion while notably lacking theoretical guarantees, leading to potential deployment risks in this field. Is it possible to conduct dynamic image fusion with a clear theoretical justification? In this paper, we give our solution from a generalization perspective. We proceed to reveal the generalized form of image fusion and derive a new test-time dynamic image fusion paradigm. It provably reduces the upper bound of generalization error. Specifically, we decompose the fused image into multiple components corresponding to its source data. The decomposed components represent the effective information from the source data, thus the gap between them reflects the Relative Dominability (RD) of the uni-source data in constructing the fusion image. Theoretically, we prove that the key to reducing generalization error hinges on the negative correlation between the RD-based fusion weight and the uni-source reconstruction loss. Intuitively, RD dynamically highlights the dominant regions of each source and can be naturally converted to the corresponding fusion weight, achieving robust results. Extensive experiments and discussions with in-depth analysis on multiple benchmarks confirm our findings and superiority. Our code is available at https://***/Yinan-Xia/TTD. © 2024 Neural information processing systems foundation. All rights reserved.

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Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI reconstruction

Recurrent Variational Network: A Deep Learning Inverse Probl...

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

作者： Yiasemis, George Sonke, Jan-Jakob Sanchez, Clarisa Teuwen, Jonas Netherlands Canc Inst Amsterdam Netherlands Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours. However, MRI suffers from very long acquisition times that make it susceptible to patient motion artifacts and limit its potential to deliver dynamic treatments. Conventional approaches such as Parallel Imaging and Compressed Sensing allow for an increase in MRI acquisition speed by reconstructing MR images from subsampled MRI data acquired using multiple receiver coils. Recent advancements in Deep Learning combined with Parallel Imaging and Compressed Sensing techniques have the potential to produce high-fidelity reconstructions from highly accelerated MRI data. In this work we present a novel Deep Learning-based Inverse Problem solver applied to the task of Accelerated MRI reconstruction, called the Recurrent Variational Network (RecurrentVarNet), by exploiting the properties of Convolutional Recurrent Neural Networks and unrolled algorithms for solving Inverse Problems. The RecurrentVarNet consists of multiple recurrent blocks, each responsible for one iteration of the unrolled variational optimization scheme for solving the inverse problem of multi-coil Accelerated MRI reconstruction. Contrary to traditional approaches, the optimization steps are performed in the observation domain (k-space) instead of the image domain. Each block of the RecurrentVarNet refines the observed k -space and comprises a data consistency term and a recurrent unit which takes as input a learned hidden state and the prediction of the previous block. Our proposed method achieves new state of the art qualitative and quantitative reconstruction results on 5-fold and 10-fold accelerated data from a public multi-coil brain dataset, outperforming previous conventional and deep learning-based approaches.

关键词： Deep learning Recurrent neural networks Inverse problems Magnetic resonance imaging Computer architecture Task analysis image reconstruction

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Proceedings - 2024 IEEE Visualization conference - Short Papers, VIS 2024

Proceedings - 2024 IEEE Visualization Conference - Short Pap...

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2024 IEEE Visualization and Visual Analytics conference, VIS 2024

ISBN: (纸本)9798350354850

The proceedings contain 66 papers. The topics discussed include: bringing data into the conversation: adapting content from business intelligence dashboards for threaded collaboration platforms;AEye: a visualization tool for image datasets;feature clock: high-dimensional effects in two-dimensional plots;curve segment neighborhood-based vector field exploration;micro visualizations on a smartwatch: assessing reading performance while walking;guided statistical workflows with interactive explanations and assumption checking;opening the black box of 3D reconstruction error analysis with VECTOR;and an Overview+Detail layout for visualizing compound graphs.

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Ensemble image Super-Resolution CNNs for Small data and Diverse Compressive Models 18th

Ensemble Image Super-Resolution CNNs for Small Data and Dive...

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18th International conference on Advanced data Mining and Applications (ADMA)

作者： Liu, Yingnan Paffenroth, Randy Clinton Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9783031221361;9781665437943

Convolutional neural networks (CNN) have become some of the most powerful tools for image reconstruction problems thanks to the availability of very large data sets. Implementations of deep residual structures, adversarial generation networks and attention mechanisms have made great accomplishment. However, the good performance from complex and deep network architecture is not guaranteed when the training data set is small and not well preventative for the entire population. There are many real-world image reconstruction tasks where large and diverse training data is unavailable, such as problems in the physical sciences and engineering for which the data set generation process is complicated and large data sets are expensive to construct. For example, herein we discuss the application of deep-learning to challenging problems in material science. Inspired by compressive sensing and ensemble learning, we propose a method using ensemble image super-resolution CNNs in transform domains to overcome the challenges of small training data in image reconstruction problems. Ensemble methods provide a more robust approach when CNNs are trained with less representative data. Transform domains could support the CNNs with multiple sparse representations of the original image data which enrich the information so that the CNNs can be sufficiently trained even using small data sets. Particularly, we report here a successful application of CNN ensembles to the reconstruction of areal density maps of carbon nano-tube sheet materials. We show that applying the ensemble CNNs in transform domains can reveal finer details in the material texture and help to improve the quality control capabilities for carbon nano-tube sheet production with only a small collection of training data.

关键词： image reconstruction Ensemble Transform domain

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Separable Modulation Network for Efficient image Super-Resolution 23

Separable Modulation Network for Efficient Image Super-Resol...

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31st ACM International conference on Multimedia (MM)

作者： Wu, Zhijian Li, Jun Huang, Dingjiang East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China Nanjing Normal Univ Sch Comp & Elect Informat Nanjing Peoples R China

ISBN: (纸本)9798400701085

Deep learning-based models have demonstrated unprecedented success in image super-resolution (SR) tasks. However, more attention has been paid to lightweight SR models lately, due to the increasing demand for on-device inference. In this paper, we propose a novel Separable Modulation Network (SMN) for efficient image SR. The key parts of the SMN are the Separable Modulation Unit (SMU) and the Locality Self-enhanced Network (LSN). SMU enables global relational interactions but significantly eases the process by separating spatial modulation from channel aggregation, hence making the long-range interaction efficient. Specifically, spatial modulation extracts global contexts from spatial, and channel aggregation condenses all global context features into the channel modulator, ultimately the aggregated contexts are fused into the final features. In addition, LSN allows guiding the network to focus on more refined image attributes by encoding local contextual information. By coupling two complementary components, SMN can capture both short- and long-range contexts for accurate image reconstruction. Extensive experimental results demonstrate that our SMN achieves state-of-the-art performance among the existing efficient SR methods with less complexity.

关键词： Deep learning image super-resolution Attention Efficiency

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