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

共 14698 条记录，以下是121-130 订阅

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IOVS4NeRF: Incremental Optimal View Selection for Large-Scale NeRFs

IOVS4NeRF: Incremental Optimal View Selection for Large-Scal...

引用

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

作者： Jingpeng Xie Shiyu Tan Yuanlei Wang Tianle Du Yifei Xue Yizhen Lao Hunan University Changsha China Nanchang University Nanchang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Large-scale Neural Radiance Fields (NeRF) reconstructions are typically hindered by the requirement for extensive image datasets and substantial computational resources. This paper introduces IOVS4NeRF, a framework that employs an uncertainty-guided incremental optimal view selection strategy adaptable to various NeRF implementations. Specifically, by leveraging a hybrid uncertainty model that combines rendering and positional uncertainties, the proposed method calculates the most informative view from among the candidates, thereby enabling incremental optimization of scene reconstruction. Our detailed experiments demonstrate that IOVS4NeRF achieves high-fidelity NeRF reconstruction with minimal computational resources, making it suitable for large-scale scene applications.

关键词： Training Uncertainty Training data Signal processing Neural radiance field Rendering (computer graphics) Robustness Trajectory Speech processing image reconstruction

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PARALLEL image-reconstruction FOR 3D POSITRON EMISSION TOMOGRAPHY from incomplete 2D PROJECTION data

PARALLEL IMAGE-RECONSTRUCTION FOR 3D POSITRON EMISSION TOMOG...

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CONF ON BIOMEDICAL image PROCESSING AND BIOMEDICAL VISUALIZATION

作者： GUERRERO, TM RICCI, AR DAHLBOM, M CHERRY, SR HOFFMAN, EJ The Crump Institute of Biological Imaging and UCLA School of Medicine (United States)

ISBN: (纸本)0819411388

The problem of excessive computational time in 3D Positron Emission Tomography (3D PET) reconstruction is defined, and we present an approach for solving this problem through the construction of an inexpensive parallel processing system and the adoption of the FAVOR algorithm. Currently, the 3D reconstruction of the 610 images of a total body procedure would require 80 hours and the 3D reconstruction of the 620 images of a dynamic study would require 110 hours. An inexpensive parallel processing system for 3D PET reconstruction is constructed from the integration of board level products from multiple vendors. The system achieves its computational performance through the use of 6U VME four i860 processor boards, the processor boards from five manufacturers are discussed from our perspective. The new 3D PET reconstruction algorithm FAVOR, FAst VOlume Reconstructor, that promises a substantial speed improvement is adopted. Preliminary results from parallelizing FAVOR are utilized in formulating architectural improvements for this problem. In summary, we are addressing the problem of excessive computational time in 3D PET image reconstruction, through the construction of an inexpensive parallel processing system and the parallelization of a 3D reconstruction algorithm that uses the incomplete data set that is produced by current PET systems.

关键词： Positron emission tomography Computing systems image filtering 3D image processing reconstruction algorithms 3D image reconstruction 3D modeling image processing Parallel processing Scanners

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Study on YSR Model for Detecting the Mandibular Strap of Safety Helmet

Study on YSR Model for Detecting the Mandibular Strap of Saf...

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Power Electronics, Computer Applications (ICPECA), IEEE International conference on

作者： Haisheng Li Lei Li Xinjian Lv Ran Zhao Shandong Hi-Speed Construction Management Group Co. Ltd. Jinan China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In the field of public infrastructure development, safety helmet is an important guarantee for the life safety of front-line construction workers. Aiming at the problem of safety helmet wearing standards, we proposes a YSR safety helmet jaw band detection model based on (YOLOv7, image superresolution reconstruction (SA-SRGAN) and classification network model (SA-ResNet). The algorithm firstly locates and extracts the helmet in the monitoring image based on YOLOv7 network model. Secondly, SA-SRGAN network with attention mechanism is used to improve the image resolution. Finally, SAResNet, which introduces attention mechanism, is used to identify the wearing standard of the helmet jaw strap. At the same time, the data set of construction site is made to verify the reliability of the proposed algorithm. Findings from the experiments indicate that the YOLOv7 model boasts an average precision of $\mathbf{9 8. 7 8 \%}$ . The reconstruction effect of SA-SRGAN model was the best, and its subjective evaluation standard score was 3.69. The classification accuracy of SA-ResNet was 98.83 %. Therefore, based on monitoring images, our method can effectively identify whether the helmet jaw strap is worn properly, with its recognition accuracy fulfilling the requirements for construction site monitoring.

关键词： Head Attention mechanisms Accuracy Shape Superresolution Feature extraction Safety image reconstruction Standards Monitoring

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FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-Language Pre-training

FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-...

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

作者： Jiale Huang Dehong Gao Jinxia Zhang Zechao Zhan Yang Hu Xin Wang Key Laboratory of Measurement and Control of CSE School of Automation Southeast University China Northwestern Polytechnical University China Binjiang Institute of Artificial Intelligence ZJUT Hangzhou China Advanced Ocean Institute of Southeast University Nantong China Alibaba Group China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Large-scale Vision-Language Pre-training (VLP) has demonstrated remarkable success in the general domain. However, in the fashion domain, items are distinguished by fine-grained attributes such as texture and material, which are crucial for tasks such as retrieval. Existing models often fail to take advantage of these fine-grained attributes from both text and image modalities. To address the above issue, we propose a novel approach for the fashion domain, Fine-grained Attributes Enhanced VLP (FashionFAE), which focuses on the detailed characteristics of the fashion data. An attribute-emphasized text prediction task is proposed to predict fine-grained attributes of the items. This forces the model to focus on the salient attributes from the text modality. In addition, a novel attribute-promoted image reconstruction task is proposed, which further enhances the fine-grained ability of the model by leveraging the representative attributes from the image modality. Extensive experiments show that FashionFAE outperforms State-Of-The-Art (SOTA) methods, achieving 2.9% and 5.2% improvements in retrieval on sub-test set and full test set, respectively, and an average improvement of 1.6% in recognition tasks.

关键词： Signal processing Predictive models Acoustics data mining Speech processing image reconstruction

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Supplementary Material AnonyNoise: Anonymizing Event data with Smart Noise to Outsmart Re-Identification and Preserve Privacy

Supplementary Material AnonyNoise: Anonymizing Event Data wi...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Katharina Bendig René Schuster Nicole Thiemer Karen Joisten Didier Stricker DFKI- German Research Center for Artificial Intelligence RPTU - University of Kaiserslautern-Landau

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In this supplementary material, we provide a more detailed overview of AnonyNoise, a method developed for predicting data-dependent noise aimed at preventing reidentification. The document is structured as follows: First, we detail the training parameters used in our implementation for the three datasets: DVS-Gesture [4], SEE [29], and Event-ReId [1], in order to ensure reproducibility. Next, we present numerical results from an inversion attack on our method, comparing its effectiveness to Gaussian noise when evaluated using a denoising network. This comparison provides insight into the robustness of AnonyNoise in contrast to traditional noise techniques in preventing data recovery and re-identification attempts. We moreover provide our statement regarding our responsibility to human subjects in the datasets used during our experiments. Lastly, we include an expanded set of visual examples across all datasets, including the results from image reconstruction attacks.

关键词： Training Visualization data privacy Computer vision Gaussian noise Noise reduction Robustness Reproducibility of results image reconstruction

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Subsampling Decomposition based k-Space Refinement for Accelerated MRI reconstruction

Subsampling Decomposition based k-Space Refinement for Accel...

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

作者： Xiaoyu Qiao Weisheng Li Bin Xiao Yuping Huang Lijian Yang Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In accelerated MRI reconstruction problem, directly recovering all the missing k-space data from undersampled measurements is highly ill-posed and often leads to suboptimal performance. To address the problem, we propose a novel deep unfolding network (DUN) with subsampling decomposition (SD) based k-space refinement to mitigate the ill-posedness. Our method employs a parallel network architecture with a primary branch unfolded by gradient descent-inspired optimization process (GD-PB) for reconstruction. Additionally, we introduce an SD-based auxiliary branch (SD-AB) that decompose the inverse problem into moderately corrupted subproblems. We design a novel subsampling mask predictor that captures both global and local spatial correlations in k-space, ensuring the SD-AB effectively preserves the most well-reconstructed subsets as reliable region (RR). The RR in SD-AB is used to periodically refine the intermediate outputs of the GD-PB, achieving improved accuracy. Experimental results reveal that our method significantly outperforms conventional and SD-based DUN techniques, achieving superior PSNR and SSIM results compared with cutting-edge methods.

关键词： Correlation Inverse problems Magnetic resonance imaging Signal processing Network architecture Reliability engineering Sensors Speech processing image reconstruction Optimization

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SALVE: A 3D reconstruction Benchmark of Wounds from Consumer-Grade Videos

SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Remi Chierchia Leo Lebrat David Ahmedt-Aristizabal Olivier Salvado Clinton Fookes Rodrigo Santa Cruz Queensland University of Technology CSIRO Data61

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Managing chronic wounds is a global challenge that can be alleviated by the adoption of automatic systems for clinical wound assessment from consumer-grade videos. While 2D image analysis approaches are insufficient for handling the 3D features of wounds, existing approaches utilizing 3D reconstruction methods have not been thor-oughly evaluated. To address this gap, this paper presents a comprehensive study on 3D wound reconstruction from consumer-grade videos. Specifically, we introduce the SALVE dataset, comprising video recordings of realistic wound phantoms captured with different cameras. Using this dataset, we assess the accuracy and precision of state-of-the-art methods for 3D reconstruction, ranging from traditional photogrammetry pipelines to advanced neural rendering approaches. In our experiments, we observe that photogrammetry approaches do not provide smooth surfaces suitable for precise clinical measurements of wounds. Neural rendering approaches show promise in addressing this issue, advancing the use of this technology in wound care practices. We encourage the readers to visit the project page: https://***/SALVE/.

关键词： Ethics Computer vision Three-dimensional displays Accuracy Protocols Wounds Rendering (computer graphics) Photogrammetry image reconstruction Videos

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Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method

Learning Diffusion Model from Noisy Measurement using Princi...

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

作者： Weimin Bai Weiheng Tang Enze Ye Siyi Chen Wenzheng Chen He Sun Academy for Advanced Studies Peking University School of Physics Peking University College of Future Technology Peking University Wangxuan Institute of Computer Technology Peking University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems. However, their reliance on large-scale clean datasets for training limits their applicability in scenarios where acquiring clean data is costly or impractical. Recent approaches have attempted to learn diffusion models directly from corrupted measurements, but these methods either lack theoretical convergence guarantees or are restricted to specific types of data corruption. In this paper, we propose a principled expectation-maximization (EM) framework that iteratively learns diffusion models from noisy data with arbitrary corruption types. Our framework employs a plug-and-play Monte Carlo method to accurately estimate clean images from noisy measurements, followed by training the diffusion model using the reconstructed images. This process alternates between estimation and training until convergence. We evaluate the performance of our method across various imaging tasks, including inpainting, denoising, and deblurring. Experimental results demonstrate that our approach enables the learning of high-fidelity diffusion priors from noisy data, significantly enhancing reconstruction quality in imaging inverse problems.

关键词： Training Inverse problems Imaging Signal processing algorithms Diffusion models Acoustic measurements Noise measurement Speech processing image reconstruction Convergence

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Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy image reconstruction

Deep Unfolded Approximate Message Passing for Quantitative A...

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

作者： Odysseas Pappas Jonathan Mamou Adrian Basarab Denis Kouamé Alin Achim Visual Information Laboratory University of Bristol Bristol UK Department of Radiology Weill Cornell Medicine New York USA CREATIS University of Lyon Lyon France IRIT University of Toulouse III Paul Sabatier Toulouse France

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at microscopy scale. Increased frequency QAM allows for finer resolution at the expense of increased acquisition times and data storage cost. Compressive sampling (CS) methods have been employed to produce QAM images from a reduced sample set, with recent state of the art utilising Approximate Message Passing (AMP) methods. In this paper we investigate the use of AMP-Net, a deep unfolded model for AMP, for the CS reconstruction of QAM parametric maps. Results indicate that AMP-Net can offer superior reconstruction performance even in its stock configuration trained on natural imagery (up to 63% in terms of PSNR), while avoiding the emergence of sampling pattern related artefacts.

关键词： Training Time-frequency analysis Ultrasonic imaging Quadrature amplitude modulation Microscopy Message passing Two-dimensional displays Acoustics Speech processing image reconstruction

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High-quality 3D generation method of ship targets based on multi-view diffusion model

High-quality 3D generation method of ship targets based on m...

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International conference on Computer Research and Development (ICCRD)

作者： Qian Li Xiuzhi Chen Shengzheng Wang Merchant Marine Academy Shanghai Maritime University Shanghai China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

The 3D reconstruction of ship targets in shipping is very important for improving the ship's perception ability and autonomous navigation ability. However, the 3D reconstruction of ship targets in the shipping field is still a challenging task. This paper proposes a framework based on a multi-view diffusion model for the 3D reconstruction of ship targets. The framework uses a multi-view diffusion model and a multi-view reconstruction model to reconstruct the 3D model of the target ship from a single-view image, which can effectively extract the shape features and surface texture of the ship. The multi-view diffusion model generates multi-view images based on a stable diffusion model. The model is trained and adjusted using high-quality ship 3D data to improve the ability to generate multi-views. The results are provided to the multi-view reconstruction model to improve its reconstruction quality. Experimental results show that the proposed method can effectively realize the 3D reconstruction of marine ship targets in real time, and is superior to existing methods in terms of geometric shape, surface smoothness and texture quality. The proposed method significantly improves the 3D reconstruction quality of single-view ship images. By fine-tuning the multi-view diffusion model for generating ship scenes, the ability to generate multi-view ship images is improved. It has the ability to generate high-quality ship 3D models and can effectively improve the 3D perception ability of autonomous ships.

关键词： Solid modeling Surface reconstruction Three-dimensional displays Shape Navigation Diffusion models Surface texture Marine vehicles image reconstruction Autonomous vehicles

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