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

共 14740 条记录，以下是221-230 订阅

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BeSplat: Gaussian Splatting from a Single Blurry image and Event Stream

BeSplat: Gaussian Splatting from a Single Blurry Image and E...

引用

IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Gopi Raju Matta Trisha Reddypalli Kaushik Mitra IIT Madras IIIT RGUKT RK Valley

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Novel view synthesis has been greatly enhanced by the development of radiance field methods. The introduction of 3D Gaussian Splatting (3DGS) has effectively addressed key challenges, such as long training times and slow ren-dering speeds, typically associated with Neural Radiance Fields (NeRF), while maintaining high-quality reconstructions. In this work (BeSplat), we demonstrate the recov-ery of sharp radiance field (Gaussian splats) from a single motion-blurred image and its corresponding event stream. Our method jointly learns the scene representation via Gaussian Splatting and recovers the camera motion through Bezier SE(3) formulation effectively, minimizing discrepan-cies between synthesized and real-world measurements of both blurry image and corresponding event stream. We evaluate our approach on both synthetic and real datasets, showcasing its ability to render view-consistent, sharp images from the learned radiance field and the estimated camera trajectory. To the best of our knowledge, ours is the first work to address this highly challenging ill-posed problem in a Gaussian Splatting framework with the effective incor-poration of temporal information captured using the event stream.

关键词： Training Three-dimensional displays Streaming media Cameras Neural radiance field Rendering (computer graphics) Robustness Trajectory image reconstruction Synthetic data

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TIRPL: Tailored-Made Inverse Rendering for Point-Light Scenes

TIRPL: Tailored-Made Inverse Rendering for Point-Light Scene...

引用

International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zonglin Tian Sicong Cheng Junli Zhao Fuqing Duan School of Artificial Intelligence Beijing Normal University China College of Computer Science and Technology Qingdao University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Inverse rendering has been extensively explored with the advent of neural implicit fields. However, existing methods struggle to model global illumination and to fully integrate volume rendering with physically based rendering in a single stage. To address this issue, we propose TIRPL, a tailored-made inverse rendering method for point-light scenes. Our method efficiently combines volume rendering and physically based rendering to jointly optimize scene geometry, materials, and global illumination from multi-view images in a single stage. We design an indirect color network and allow the light intensity to be learnable to estimate global illumination. Additionally, a feature vector and a tailored-made light intensity decay rate are devised to combine volume rendering and physically based rendering in a single stage. Extensive experiments are conducted in both real and synthetic datasets, showing the effectiveness of our method.

关键词： Geometry Solid modeling image color analysis Lighting Signal processing Rendering (computer graphics) Vectors Speech processing image reconstruction Synthetic data

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Robust Calibration for Enhanced Imaging in Near-Field D-Band MIMO Radar Systems

Robust Calibration for Enhanced Imaging in Near-Field D-Band...

引用

European conference on Antennas and Propagation, EuCAP

作者： Lorena Perez-Eijo Marcos Arias Borja Gonzalez-Valdes José Vázquez-Cabo Ignacio Sardinero–Meirás Jesus Grajal AtlanTTic Research Center Universidade de Vigo Vigo Spain Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This work introduces a calibration algorithm designed to improve high-precision imaging in real-time MIMO radar systems. The algorithm applies the Inverse Fourier Transform (IFFT) and advanced signal processing techniques to efficiently extract and process key data from raw measurements, focusing on critical Regions Of Interest (ROI) while filtering out unnecessary information. A robust phase correction mechanism ensures precise calibration and alignment, preventing phase errors that could otherwise lower image quality. The algorithm provides an effective balance between computational efficiency and the high accuracy required by modern MIMO radars, paving the way for further improvements in real-time imaging.

关键词： MIMO radar Signal processing algorithms Coherence Radar imaging Signal processing Real-time systems Hardware Calibration Spatial resolution image reconstruction

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image Guided Lidar Point Cloud Completion Algorithm

Image Guided Lidar Point Cloud Completion Algorithm

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International conference on

作者： Shangtai Gu Ke Xu Baolin Hou Jianwei Wan College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

To address the issues of severe information loss and suboptimal fusion effects in multimodal feature extraction and integration during point cloud shape completion using autoen-coder structures, which results in difficulty in balancing local and global feature information of point clouds and significant loss of structural information in images, this paper proposes a LiDAR point cloud multi-scale completion algorithm guided by image rotation attention mechanisms, with a focus on the study of feature extraction from point clouds and images. The network employs an encoder-decoder structure, where the image feature extractor in the encoder utilizes rotation attention mechanisms to enhance the capability of image feature extraction. The point cloud feature extractor employs multi-scale methods to improve the global and local information of point cloud features and employs multi-level self-attention mechanisms to achieve multimodal feature fusion. The decoder then employs a multi-branch completion method to accomplish the point cloud completion task, with the network trained using chamfer distance guidance. Comparatively, our algorithm outperforms eight related algorithms on the ShapeNet-ViPC dataset across various metrics. Compared to the state-of-the-art network XMFnet, the category-averaged Chamfer Distance (CD) value is reduced by 11.71%. The proposed algorithm in this paper can better extract image structural information, and the feature extraction of incomplete point clouds can consider both global and local information. Furthermore, through multi-level progressive feature fusion, the algorithm enhances the complementarity of information between different modalities, leading to more accurate point cloud completion results.

关键词： Point cloud compression Training Measurement Laser radar Attention mechanisms Accuracy Shape Feature extraction Decoding data mining

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DeepCA: Deep Learning-Based 3D Coronary Artery Tree reconstruction from Two 2D Non-Simultaneous X-Ray Angiography Projections

DeepCA: Deep Learning-Based 3D Coronary Artery Tree Reconstr...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Yiying Wang Abhirup Banerjee Robin P. Choudhury Vicente Grau Department of Engineering Science Institute of Biomedical Engineering University of Oxford UK Radcliffe Department of Medicine Division of Cardiovascular Medicine University of Oxford UK

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Cardiovascular diseases (CVDs) are the most common cause of death worldwide. Invasive x-ray coronary angiography (ICA) is one of the most important imaging modalities for the diagnosis of CVDs. ICA typically acquires only two 2D projections, which makes the 3D geometry of coronary vessels difficult to interpret, thus requiring 3D coronary artery tree reconstruction from two projections. State-of-the-art approaches require significant manual interactions and cannot correct the non-rigid cardiac and respiratory motions between non-simultaneous projections. In this study, we propose a novel deep learning pipeline named DeepCA. We leverage the Wasserstein conditional generative adversarial network with gradient penalty, latent convolutional transformer layers, and a dynamic snake convolutional critic to implicitly compensate for the non-rigid motion and provide 3D coronary artery tree reconstruction. Through simulating projections from coronary computed tomography angiography (CCTA), we achieve the generalisation of 3D coronary tree reconstruction on real non-simultaneous ICA projections. We incorporate an application-specific evaluation metric to validate our proposed model on both a CCTA dataset and a real ICA dataset, together with Chamfer £2 distance. The results demonstrate promising performance of our DeepCA model in vessel topology preservation, recovery of missing features, and generalisation ability to real ICA data. To the best of our knowledge, this is the first study that leverages deep learning to achieve 3D coronary tree reconstruction from two real non-simultaneous x-ray angiographic projections. The implementation of this work is available at: https//***/WangStephen/DeepCA.

关键词： Deep learning Three-dimensional displays Angiography Pipelines Transformers Real-time systems Topology image reconstruction Arteries X-ray imaging

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20th International conference on Information Security and Cryptology, Inscrypt 2024

20th International Conference on Information Security and Cr...

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20th International conference on Information Security and Cryptology, Inscrypt 2024

ISBN: (纸本)9789819647330

The proceedings contain 23 papers. The special focus in this conference is on Information Security and Cryptology. The topics include: TA-DPDP: Dynamic Provable data Possession for Online Collaborative System with Tractable Anonymous;on the Relationship Between Public Key Primitives via Indifferentiability;lattice-Based Succinct Mercurial Functional Commitment for Boolean Circuits: Definitions, and Constructions;from Signature with Re-randomizable Keys: Generic Construction of PDPKS;SIMD Optimizations of White-Box Block Cipher Implementations with the Self-equivalence Framework;when Is Multi-channel Better Than Single-Channel: A Case Study of Product-Based Multi-channel Fusion Attacks;privacy-Preserving Certificate-Less Authenticated Key Exchange with Key Registration Privacy;An Improved Signal Leakage Attack Against DXL Key Exchange Protocol;srCPace: Universally Composable PAKE with Subversion-Resilience;Analyzing Decentralized Applications Traffic: A Multimodal Approach Based on GNN and BERT;EditPSM: A New Password Strength Meter Based on Password Reuse via Deep Learning;a Binary Code Similarity Detection Method Based on Multi-source Contrastive Learning;malware Detection Method Based on image Sample reconstruction and Feature Enhancement;attention-Based Decompilation Through Neural Machine Translation;DBridger: Discovering Vulnerable data Sharing Paths in Embedded Firmware;efficient Privacy-Preserving data Sharing Mechanisms Against Malicious Senders in Smart Grid;NEST: Strong Key-Insulated Password-Based Shared-Custodial Blockchain Wallets;clustering Coefficient Estimating of Distributed Graph data Based on Shuffled Differential Privacy;Public-Key Watermarkable PRFs with Dynamic Bounded Collusion;a Lossless Relational data Watermarking Scheme Based on Uneven Partitioning;CNNOVZKP: Convolutional Neural Network Model Ownership Verification with Zero-Knowledge Proof.

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Spatio-Temporal Mapping Generative Adversarial Network for Functional Connectivity Network reconstruction across Brain Atlases

Spatio-Temporal Mapping Generative Adversarial Network for F...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hongzheng Guan Tao Jin Li Xiao Gang Qu Yu-Ping Wang Institute of Advanced Technology University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Biomedical Engineering Tulane University New Orleans LA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Functional connectivity networks (FCNs), as graph-structured data derived from functional magnetic resonance imaging (fMRI), are essential for understanding how brain functions coordinate with behavior and cognition. However, the utility of these FCNs is often limited by the brain atlas, since the predefined regions of interest by the atlas represent nodes in FCNs. To address these limitations and enhance the comparability of functional connectivity analyses across different atlases, we introduce the Spatio-Temporal Mapping Generative Adversarial Network (STMap-GAN) based on generative modeling. Convolutional networks and long short-term memory modules are used in the generator to improve the spatio and temporal consistency of generated fMRI time series for target brain atlases. The transformer module in the discriminator can effectively capture different features in fMRI time series, thus accurately distinguishing the generated time series from ground truth. This study demonstrates the ability of STMap-GAN to maintain high fidelity in FCN mapping across various atlases, ensuring consistency and replicability in neuroscience research.

关键词： Time series analysis Functional magnetic resonance imaging Signal processing Transformers Generative adversarial networks Convolutional neural networks Speech processing image reconstruction Long short term memory Replicability

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Improving Compressive Imaging Recovery via Measurement Augmentation

Improving Compressive Imaging Recovery via Measurement Augme...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Romario Gualdrón-Hurtado Roman Jacome Leon Suarez Emmanuel Martinez Henry Arguello Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In compressive imaging systems, the scene is acquired via linear coded noisy projections, known as measurements, requiring a recovery process to estimate the underlying signal. This recovery is inherently ill-posed, posing a challenge for accurate signal recovery. Existing methods that employ prior information about the signal often fail in practical scenarios. In this work, instead of developing a new prior over the signal, we exploit the structure of the low-dimensional measurements to synthesize an augmented measurement set that can be used in various recovery methods to improve its performance. We used a deep neural network to generate the synthetic measurements from the acquired data. We show the benefits of this approach in two schemes, deep learning-based recovery and the plug-and-play (PnP) algorithm. Particularly, our method is interpreted as a non-linear preconditioning technique for the PnP algorithm. We show improved performance for different sensing matrices.

关键词： image coding Atmospheric measurements Imaging Signal processing algorithms Particle measurements Acoustic measurements Sensors Noise measurement Speech processing image reconstruction

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Detection of Brain Tumor Using Transfer Learning Using Conventional Autoencoder with Long Short Term Memory Method

Detection of Brain Tumor Using Transfer Learning Using Conve...

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Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： D Agalya S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Brain tumor (BT) has generate a significant health challenge by putting pressure on healthy brain parts or spreading into other areas as well as blocking the flow of fluid around the brain. BT diagnosis is an extensive and time consumption process that depends primarily on radiologists experience and interpretive capabilities. For the area of medical imaging, automatic segmentation of images by means of Deep Learning (DL) approaches. The DL approaches assist in transforming the sector, resulted in improving precision and effectiveness of investigations. However, there are various feature extraction mechanisms available. Therefore, the Recurrent Neural Network (RNN) is utilized for feature extraction and image classification. In model execution, the data have trained in transforming the test image as well as data features for minimizing the domain shift is calculated through the Convolutional Autoencoder (CAE) for reconstruction loss. This research has concentrated in building a model with VGG16 as a single test that subjected at inference and existing method is adopted as neural networks for AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. The AE used to train from the source dataset and perform as the adaptors in optimizing during testing using a test subject for effective computation. Moreover, the Long Short Term Memory (LSTM) is utilized as RNN model with CAE for providing improved detection of BT in health care industries. Hence, the proposed CAE with LSTM is compared with AE with Convolutional Neural Network (CNN) for evaluating BT detection using MRI dataset with various BT type classifications.

关键词： Training Recurrent neural networks Computational modeling Autoencoders Transfer learning Predictive models Convolutional neural networks Long short term memory image classification Testing

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RGB2Point: 3D Point Cloud Generation from Single RGB images

RGB2Point: 3D Point Cloud Generation from Single RGB Images

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jae Joong Lee Bedrich Benes Department of Computer Science Purdue University West Lafayette USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

We introduce RGB2Point, an unposed single-view RGB image to a 3D point cloud generation based on Transformer. RGB2Point takes an input image of an object and generates a dense 3D point cloud. Contrary to prior works based on CNN layers and diffusion-denoising approaches, we use pretrained Transformer layers that are fast and generate high-quality point clouds with consistent quality over available categories. Our generated point clouds demonstrate high quality on a real-world dataset, as evidenced by improved Chamfer distance (51.15%) and Earth Mover's distance (36.17%) metrics compared to the current state-of-the-art. Additionally, our approach shows a better quality on a synthetic dataset, achieving better Chamfer distance (39.26%), Earth Mover's distance (26.95%), and F-score (47.16%). Moreover, our method produces 63.1% more consistent high-quality results across various object categories compared to prior works. Furthermore, RGB2Point is computationally efficient, requiring only 2.3GB of VRAM to reconstruct a 3D point cloud from a single RGB image, and our implementation generates the results 15,133 x faster than a SOTA diffusion-based model.

关键词： Point cloud compression Earth Measurement Solid modeling Computer vision Three-dimensional displays Computational modeling Transformers image reconstruction Synthetic data

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