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

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Phenotypic Parameters Extraction of Panax notoginseng with NeRF 12

Phenotypic Parameters Extraction of Panax notoginseng with N...

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12th International conference on Information Systems and Computing Technology

作者： Wang, Ruoxi Dai, Liang Yang, Qiliang Yang, Ling Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Yunnan Peoples R China

ISBN: (纸本)9798350379860;9798350379877

The growth stage significantly influences both the yield and quality of Panax notoginseng. Accurate plant phenotypic parameters are crucial for the precise management of P. notoginseng cultivation. Currently, traditional methods like manual measurements and tools are used for collecting phenotypic information, but they are inefficient and costly. Manual measurements are prone to subjective biases and can potentially harm plants irreversibly. While 2D images can capture plant phenotypic information, they often suffer from incomplete data and lack precision, especially for plants with complex structures like P. notoginseng. This limitation makes it challenging to achieve comprehensive and accurate measurements. To address these challenges, this study proposes a novel approach using a Neural Radiance Field (NeRF) for extracting phenotypic parameters from P. notoginseng. By capturing video and multi-view image sequences of P. notoginseng, we were able to achieve high-fidelity 3D rendering of the plants and extract point cloud data from them. This approach enabled accurate measurement of plant height and leaf area parameters. The results demonstrate promising accuracy, with an average percentage error of 1.76% for plant height and 1.73% for leaf area based on the point cloud measurements obtained using NeRF. This method leverages advanced computational techniques to overcome the limitations of traditional 2D imaging methods, offering a more comprehensive and precise means of phenotypic characterization for complex plant structures like P. notoginseng.

关键词： component Panax notoginseng image Sequence Neural Radiation Field 3D reconstruction plant phenotype

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Signal Domain Learning Approach for Optoacoustic image reconstruction from Limited view data 5

Signal Domain Learning Approach for Optoacoustic Image Recon...

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5th International conference on Medical Imaging with Deep Learning (MIDL)

作者： Susmelj, Anna Klimovskaia Lafci, Berkan Ozdemir, Firat Davoudi, Neda Dean-Ben, Xose Luis Perez-Cruz, Fernando Razansky, Daniel Swiss Fed Inst Technol Swiss Data Sci Ctr Zurich Switzerland EPFL Zurich Switzerland Univ Zurich Inst Pharmacol & Toxicol Fac Med Zurich Switzerland Univ Zurich Inst Biomed Engn Fac Med Zurich Switzerland Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Inst Biomed Engn Zurich Switzerland Swiss Fed Inst Technol Dept Comp Sci Inst Machine Learning Zurich Switzerland

Multi-spectral optoacoustic tomography (MSOT) relies on optical excitation of tissues with subsequent detection of the generated ultrasound waves. Optimal image quality in MSOT is achieved by detection of signals from a broad tomographic view. However, due to physical constraints and other cost-related considerations, most imaging systems are implemented with probes having limited tomographic coverage around the imaged object, such as linear array transducers often employed for clinical ultrasound (US) imaging. MSOT image reconstruction from limited-view data results in arc-shaped image artifacts and disrupted shape of the vascular structures. Deep learning methods have previously been used to recover MSOT images from incomplete tomographic data, albeit poor performance was attained when training with data from simulations or other imaging modalities. We propose a two-step method consisting of i) style transfer for domain adaptation between simulated and experimental MSOT signals, and ii) supervised training on simulated data to recover missing tomographic signals in realistic clinical data. The method is shown capable of correcting images reconstructed from sub-optimal probe geometries using only signal domain data without the need for training with ground truth (GT) full-view images.

关键词： Optoacoustics Limited view Artifacts Signal Domain Learning Style Transfer Domain Adaptation

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Whole-Heart reconstruction with Explicit Topology Integrated Learning 26th

Whole-Heart Reconstruction with Explicit Topology Integrated...

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

作者： Yang, Huilin Tam, Roger Tang, Xiaoying Univ British Columbia Vancouver BC V6T 1Z4 Canada Southern Univ Sci & Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031439865;9783031439872

reconstruction and visualization of cardiac structures play significant roles in computer-aided clinical practice as well as scientific research. With the advancement of medical imaging techniques, computing facilities, and deep learning models, automatically generating whole-heart meshes directly from medical imaging data becomes feasible and shows great potential. Existing works usually employ a point cloud metric, namely the Chamfer distance, as the optimization objective when reconstructing the whole-heart meshes, which nevertheless does not take the cardiac topology into consideration. Here, we propose a novel currents-represented surface loss to optimize the reconstructed mesh topology. Due to currents's favorable property of encoding the topology of a whole surface, our proposed pipeline delivers whole-heart reconstruction results with correct topology and comparable or even higher accuracy.

关键词： Whole Heart reconstruction Topology Deep Learning

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Tomographic reconstruction from sparse-view and limited-angle data using a generative adversarial network 16

Tomographic reconstruction from sparse-view and limited-angl...

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16th International conference on Signal-image Technology and Internet-Based Systems (SITIS)

作者： Ayad, Ishak Tarpau, Cecilia Cebeiro, Javier Nguyen, Mai K. CY Cergy Paris Univ ETIS UMR 8051 Cergy Pontoise France CY Cergy Paris Univ CNRS ENSEA AGM UMR 8088 Cergy Pontoise France Heriot Watt Univ Maxwell Inst Math Sci Sch Math & Comp Sci Edinburgh Scotland Univ Nacl San Martin Ctr Matemcit Aplicada Inst Tecnol Ciencias Aplicadas ITECA UNSAM CONICETEscuela Ciencia & Tecnol Buenos Aires Argentina CY Cergy Paris Univ ENSEA CNRS ETIS UMR 8051 Cergy Pontoise France

ISBN: (纸本)9781665464956

Limited-angle and sparse-view computed tomography have been widely used to shorten the acquisition time in medical imaging and to offer the possibility of scanning large objects. However, this is a severely ill-posed inverse problem due to missing data. In these scenarios, the well-known filtered back-projection reconstruction technique exhibits severe artifacts and degradation. Recently, deep learning methods have demonstrated impressive performance in computer vision (denoising, classification, etc.) but it frequently fails to solve both limited-angle and sparse-view reconstruction. Inspired by the high performance of GAN-based image-to-image translation methods, we investigate a patchGAN as a solution to the reconstruction problem mapping data (Radon space) into the image domain. The generator is made of a v-net where the reconstruction in the sense of a least-squares minimization is carried out at different scales in the encoder path and linked with the decoder path by skip connections. The discriminator uses both information from the image and projection data domains. The proposed method gives promising reconstruction results from data acquired with a limited angular range covering only 110 degrees (instead of 180 degrees), as well as for sparse-view data with 10 degrees of sampling step. Moreover, different reconstruction results show that the method is able to reconstruct images from sparse and limited angular range data at the same time.

关键词： computed tomography (CT) generative adversarial networks (GAN) image reconstruction inverse problem hybrid representations

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Med-DANet v2: A Flexible Dynamic Architecture for Efficient Medical volumetric Segmentation

Med-DANet V2: A Flexible Dynamic Architecture for Efficient ...

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IEEE/CvF Winter conference on Applications of Computer vision (WACv)

作者： Shen, Haoran Zhang, Yifu Wang, Wenxuan Chen, Chen Liu, Jing Song, Shanshan Li, Jiangyun Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet [44]) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019. Code will be available at https://***/ RubicsXuan/Med- DANet.

关键词： Algorithms Applications Biomedical / healthcare / medicine image recognition and understanding

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NvINS: Robust visual Inertial Navigation Fused with NeRF-augmented Camera Pose Regressor and Uncertainty Quantification

NVINS: Robust Visual Inertial Navigation Fused with NeRF-aug...

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2024 International conference on Intelligent Robots and Systems

作者： Han, Juyeop Beyer, Lukas Lao Cavalheiro, Guilherme, v Karaman, Sertac MIT Lab Informat & Decis Syst LIDS Cambridge MA 02139 USA

ISBN: (纸本)9798350377712;9798350377705

In recent years, Neural Radiance Fields (NeRF) have emerged as a powerful tool for 3D reconstruction and novel view synthesis. However, the computational cost of NeRF rendering and degradation in quality due to the presence of artifacts pose significant challenges for its application in real-time and robust robotic tasks, especially on embedded systems. This paper introduces a novel framework that integrates NeRF-derived localization information with visual-Inertial Odometry (vIO) to provide a robust solution for real-time robotic navigation. By training an absolute pose regression network with augmented image data rendered from a NeRF and quantifying its uncertainty, our approach effectively counters positional drift and enhances system reliability. We also establish a mathematically sound foundation for combining visual inertial navigation with camera localization neural networks, considering uncertainty under a Bayesian framework. Experimental validation in a photorealistic simulation environment demonstrates significant improvements in accuracy compared to a conventional vIO approach.

关键词： Neural networks

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GTA-HDR: A Large-Scale Synthetic dataset for HDR image reconstruction

GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Recon...

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2025 IEEE/CvF Winter conference on Applications of Computer vision, WACv 2025

作者： Barua, Hrishav Bakul Stefanov, Kalin Wong, KokSheik Dhall, Abhinav Krishnasamy, Ganesh Monash University Australia Tcs Research India

ISBN: (纸本)9798331510831

High Dynamic Range (HDR) content (i.e., images and videos) has a broad range of applications. However, capturing HDR content from real-world scenes is expensive and time-consuming. Therefore, the challenging task of reconstructing visually accurate HDR images from their Low Dynamic Range (LDR) counterparts is gaining attention in the vision research community. A major challenge is the lack of datasets, which capture diverse scene conditions (e.g., lighting, weather, locations) and various image features (e.g., color, contrast, saturation). To address this gap, we introduce GTA-HDR, a large-scale synthetic dataset of photo-realistic HDR images sampled from the GTA-v video game. We perform thorough evaluation of the proposed dataset, which enables significant qualitative and quantitative improvements of the state-of-the-art HDR image reconstruction methods. Furthermore, we demonstrate the effectiveness of the proposed dataset and its impact on additional computer vision tasks including 3D human pose estimation, human body part segmentation, and holistic scene segmentation. The dataset, data collection pipeline, and evaluation code are available at: https://***/HrishavBakulBarua/GTA-HDR. © 2025 IEEE.

关键词： Deep learning

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SNeS: Learning Probably Symmetric Neural Surfaces from incomplete data 17th

SNeS: Learning Probably Symmetric Neural Surfaces from Incom...

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17th European conference on Computer vision (ECCv)

作者： Insafutdinov, Eldar Campbell, Dylan Henriques, Joao F. vedaldi, Andrea Univ Oxford Oxford OX1 3PJ England

ISBN: (纸本)9783031198236;9783031198243

We present a method for the accurate 3D reconstruction of partly-symmetric objects. We build on the strengths of recent advances in neural reconstruction and rendering such as Neural Radiance Fields (NeRF). A major shortcoming of such approaches is that they fail to reconstruct any part of the object which is not clearly visible in the training image, which is often the case for in-the-wild images and videos. When evidence is lacking, structural priors such as symmetry can be used to complete the missing information. However, exploiting such priors in neural rendering is highly non-trivial: while geometry and non-reflective materials may be symmetric, shadows and reflections from the ambient scene are not symmetric in general. To address this, we apply a soft symmetry constraint to the 3D geometry and material properties, having factored appearance into lighting, albedo colour and reflectivity. We evaluate our method on the recently introduced CO3D dataset, focusing on the car category due to the challenge of reconstructing highly-reflective materials. We show that it can reconstruct unobserved regions with high fidelity and render high-quality novel view images.

关键词： 3D reconstruction Novel view synthesis Neural rendering

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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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Improving the Identification of Layers in 3D images of Ancient Papyrus Using Artificial Neural Networks

Improving the Identification of Layers in 3D Images of Ancie...

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2025 IEEE/CvF Winter conference on Applications of Computer vision Workshops, WACvW 2025

作者： Klenert, Nicolas Schwoerer, Finn Hajarolasvadi, Noushin Bournez, Siloé Arlt, Tobias Mahnke, Heinz-Eberhard Lepper, verena Baum, Daniel Zuse Institute Berlin Department of Visual and Data-Centric Computing Berlin Germany Helmholtz-Zentrum Berlin für Materialien und Energie Berlin Germany Ägyptisches Museum und Papyrussammlung Staatliche Museen zu Berlin Berlin Germany

ISBN: (纸本)9798331536626

The non-invasive digital unfolding of ancient documents, such as folded papyrus packages, from 3D image data aims to reveal previously hidden writing without risking to damage the precious documents. One of the main tasks in this process is the geometric reconstruction of the writing sub-strate, which is a prerequisite for its subsequent unfolding. All current reconstruction methods require the existence of an interspace between different layers of the document to ensure a correct topology. Layers that appear merged to-gether in the 3D image often result in wrong connections between layers and thus also in a wrong topology of the reconstructed geometry, which hinders the successful unfolding. Here, we propose to use a neural network to facilitate the discrimination of the layers. Using papyrus documents as an example of a particularly difficult writing material, we show that our approach significantly reduces the number of wrong connections and improves the overall identification of the layers. This in turn enables fully automatic digital unfolding of large areas of highly complex papyrus packages. Utilizing explainable AI (XAI) further allows us to explore the results of the applied neural network. © 2025 IEEE.

关键词： Three dimensional computer graphics

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