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

共 622 条记录，以下是21-30 订阅

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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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GeneSpider: Inferring Gene Regulation Relationships Through Graph Neural Network from Single-Cell RNA Sequence data 1

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19th International conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Du, Zhihua Zhong, Xing Fang, Min Li, Jianqiang Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Harbin Inst Technol Shenzhen Educ Ctr Expt & Innovat Shenzhen Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

Predicting gene regulation relationships from single-cell expression data is an important problem in the field of genomics. Numerous methods have been developed to address the challenges of high dimensionality and sparsity in single-cell expression data, including machine learning and deep learning techniques. In this paper, we present a novel framework called GeneSpider for inferring gene regulation networks from single-cell RNA sequence expression data. We use Germain Angular Summation Field to encode single-cell RNA sequence expression data to image representation, which reduces the impact of sparseness and high dimensionality of single-cell RNA sequence expression data on the reconstruction of gene regulation networks. Then, we propose a novel graph neural network consisted of two convolution layers which extracts features of image nodes and topological information of their neighborhoods. It can sum the features of the extracted nodes and their neighbors as nodes new feature. Finally, the probability of regulating between transcription factors and target genes outputted through fully connected layers. We compare GeneSpider with eight state-of-the-art gene regulatory networks reconstruction methods on seven single-cell RNA sequence expression datasets with three ground-truth networks and show that GeneSpider achieves substantially better performance.

关键词： single-cell RNA sequence expression data gene regulatory network graph neural network

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image Retrieval in Semiconductor Manufacturing 1

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22nd International conference on image Analysis and Processing (ICIAP)

作者： Gatta, Giuseppe Gianmarco Carrera, Diego Rossi, Beatrice Fragneto, Pasqualina Boracchi, Giacomo STMicroelectronics Geneva Switzerland Politecn Milan Milan Italy

ISBN: (数字)9783031431487

ISBN: (纸本)9783031431470;9783031431487

Content-Based image Retrieval has a lot of applications in the industry, where large collections of data from manufacturing need to be automatically queried e.g. for quality inspection purposes. In this work we design an image retrieval solution over IMAGO, a dataset of Transmission Electron Microscopy (TEM) images of nano-sized silicon structures collected in the production site of STMicroelectronics, in Agrate Brianza, Italy. image retrieval in imago is challenging because: i) only a limited portion of images are provided with labels, namely type of semiconductor structure, ii) most images refer to unseen classes that are not represented in the training set, and iii) images of the same class can be acquired at different magnification levels of the electronic microscope. Our main contribution is the design of a deep-learning based image retrieval system that leverages a training procedure that alternates between siamese loss, assessed on annotated samples, and reconstruction loss, assessed on unlabelled samples. Our solution exploits the whole information in the IMAGO dataset, and our experiments confirm we can successfully retrieve images of unseen classes that exhibit the same structure of the query ones. Our solution is currently deployed in STMicroelectronics production sites.

关键词： Content-Based image Retrieval Siamese Networks Autoencoders

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images Structure reconstruction from fMRI by Unsupervised Learning Based on VAE 1

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31st International conference on Artificial Neural Networks (ICANN)

作者： Zhao, Zhiwei Jing, Haodong Wang, Jianji Wu, Weihua Ma, Yongqiang Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China

ISBN: (数字)9783031159343

ISBN: (纸本)9783031159343;9783031159336

How to reconstruct the stimulus images from fMRI signals is an important problem in the field of neuroscience. Limited by the complexity and the acquisition accuracy of brain signals, it is still very difficult to completely reconstruct the realistic images from fMRI signals by the artificial intelligence related technology. In the experiments related to brain signals stimulated by real images, we found that: as subjects, when stimulated by continuous realistic images, they are more sensitive to the high-frequency information in the stimulus images, such as image contour, color upheaval area, etc., The human vision and cerebral cortex seem to respond more strongly to these. Based on this discovery, we propose a method that pays more attention to the image structure to reconstruct images from fMRI signals. In order to fully decode the voxels in fMRI signals and solve the problem of insufficient amount of fMRI data, we use a back-to-back model based on Variational Auto-Encoder, which can decode more voxels in fMRI recordings into meaningful image features and introduce more unlabeled data to improve the overall performance of the model. Experiments demonstrate that our method performers better than other mainstream methods.

关键词： fMRI image structure image reconstruction Variational auto-encoder

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Registration of Longitudinal Spine CTs for Monitoring Lesion Growth

Registration of Longitudinal Spine CTs for Monitoring Lesion...

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conference on Medical Imaging - image Processing

作者： Sanhinova, Malika Haouchine, Nazim Pieper, Steve D. Wells, William M., iii Balboni, Tracy A. Spektor, Alexander Mai Anh Huynh Guenette, Jeffrey P. Czajkowski, Bryan Caplan, Sarah Doyle, Patrick Kang, Heejoo Hackney, David B. Alkalay, Ron N. Harvard Med Sch Boston MA 02115 USA Beth Israel Deaconess Med Ctr Boston MA 02215 USA Brigham & Womens Hosp 75 Francis St Boston MA 02115 USA Isomics Inc Cambridge MA USA Dana Farber Canc Inst Boston MA 02115 USA

ISBN: (纸本)9781510671577;9781510671560

Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression. Our method follows a two-step pipeline where vertebrae are first automatically localized, labeled and 3D surfaces are generated using a deep learning model, then longitudinally aligned using a Gaussian mixture model surface registration. We tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3, 6, and 12 months follow-ups leading to 111 registrations. Our experiment showed accurate registration with an average Hausdorff distance of 0.65 mm and average Dice score of 0.92.

关键词： image registration Spine data modeling 3D modeling Automatic alignment Bone Computed tomography Mixtures reconstruction algorithms Rigid registration

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In-image Neural Machine Translation with Segmented Pixel Sequence-to-Sequence Model

In-Image Neural Machine Translation with Segmented Pixel Seq...

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conference on Empirical Methods in Natural Language Processing (EMNLP)

作者： Tian, Yanzhi Li, Xiang Liu, Zeming Guo, Yuhang Wang, Bin Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Xiaomi AI Lab Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9798891760615

In-image Machine Translation (IIMT) aims to convert images containing texts from one language to another. Traditional approaches for this task are cascade methods, which utilize optical character recognition (OCR) followed by neural machine translation (NMT) and text rendering. However, the cascade methods suffer from compounding errors of OCR and NMT, leading to a decrease in translation quality. In this paper, we propose an end-to-end model instead of the OCR, NMT and text rendering pipeline. Our neural architecture adopts an encoder-decoder paradigm with segmented pixel sequences as inputs and outputs. Through end-to-end training, our model yields improvements across various dimensions, (i) it achieves higher translation quality by avoiding error propagation, (ii) it demonstrates robustness for out domain data, and (iii) it displays insensitivity to incomplete words. To validate the effectiveness of our method and support for future research, we construct our dataset containing 4M pairs of De-En images and train our end-to-end model. The experimental results show that our approach outperforms both cascade method and current end-to-end model.

关键词： Computational linguistics

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Compressive Imaging reconstruction via Conditional Diffusion Model With Augmented Measurements

Compressive Imaging Reconstruction via Conditional Diffusion...

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

作者： Emmanuel Martinez Leon Suarez Romario Gualdrón-Hurtado Roman Jacome 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

Compressive imaging (CI) consists of reconstructing images from incomplete observed data. The reconstruction process involves solving an ill-posed inverse problem which is highly dependent on the number of real measurements, with a greater number of measurements typically leading to more accurate reconstructions. Due to their ability to learn data distributions, diffusion models (DM) have emerged as promising techniques for various inverse problems. Mainly, DMs solve inverse problems by conditioning the generation process to the acquired measurements. In this work, we introduce a new approach to improve this conditioning by exploiting synthetic measurements, which come from a synthetic sensing matrix. Synthetic measurements are estimated from real data via a neural network. The combined real and synthetic measurements form an augmented set, which is input into the conditional DM to enhance reconstruction capacity. Computational experiments demonstrate that augmenting measurements with the conditional DM improves performance compared to using only real measurements.

关键词： image coding Inverse problems Imaging Signal processing Diffusion models Robustness Sensors Noise measurement Speech processing image reconstruction

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A Bottom-Up Sampling Strategy for Reconstructing Geospatial data from Ultra Sparse Inputs 9th

A Bottom-Up Sampling Strategy for Reconstructing Geospatial...

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19th International conference on Advanced data Mining and Applications, ADMA 2023

作者： Landt-Hayen, Marco Wölker, Yannick Rath, Willi Claus, Martin Christian-Albrechts-Universität zu Kiel Kiel Germany GEOMAR Helmholtz Centre for Ocean Research Kiel Germany

ISBN: (纸本)9783031466601

Working with observational data in the context of geophysics can be challenging, since we often have to deal with missing data. This requires imputation techniques in pre-processing to obtain data-mining-ready samples. Here, we present a convolutional neural network (CNN) approach from the domain of deep learning to reconstruct complete data from sparse inputs. CNN architectures are state-of-the-art for image processing. As data, we use two-dimensional fields of sea level pressure (SLP) and sea surface temperature (SST) anomalies. To have consistent data over a sufficiently long time span, we favor to work with output from control simulations of two Earth System Models (ESMs), namely the Flexible Ocean and Climate Infrastructure and the Community Earth System Model. Our networks can restore complete information from incomplete input samples with varying rates of missing data. Moreover, we present a technique to identify the most relevant grid points of our input samples. Choosing the optimal subset of grid points allows us to successfully reconstruct SLP and SST anomaly fields from ultra sparse inputs. As a proof of concept, the insights obtained from ESMs can be transferred to real world observations to improve reconstruction quality. As uncertainty measure, we compare several climate indices derived from reconstructed versus complete fields. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Earth system models

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Sentinel-2 and MODIS data Fusion for Generation of Daily Cloud-Free images at the Sentinel Resolution Level 7

Sentinel-2 and MODIS Data Fusion for Generation of Daily Clo...

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7th International conference on Computational Linguistics and Intelligent Systems. Volume iii: Intelligent Systems Workshop, CoLInS 2023

作者： Ivanchuk, Natalya Kogut, Peter Martyniuk, Petro EOS Data Analytics Ukraine Desyatynny lane 5 Kyiv01001 Ukraine Department of Computer Sciences and Applied Mathematics National University of Water and Environmental Engineering Soborna str. 11 Rivne33028 Ukraine Department of Mathematical Analysis and Optimization Oles Honchar Dnipro National University Gagarin av. 72 Dnipro49010 Ukraine EOS Data Analytics Ukraine Gagarin av. 103a Dnipro49010 Ukraine Institute of Automation Cybernetics and Computer Engineering National University of Water and Environmental Engineering Soborna str. 11 Rivne33028 Ukraine

In this paperwe discuss a newvariational approach to the Date Fusion problem of multi-spectral satellite images from Sentinel-2 and MODIS that have been captureed at different resolution level and, arguably, on different days. The crucial assumption to our approach is that the MODIS image has to be cloud-free whereas the images from Sentinel-2 can be corrupted by clouds or noise. We formulate the data fusion problem as the two-level optimization problem. We discuss the well thoroughness and consistency of the proposed variational models. We also derive some optimality conditions and supply our approach by results of numerical simulations with the real satellite images. © 2023 Copyright for this paper by its authors.

关键词： image reconstruction

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MASKED FREQUENCY MODELING FOR SELF-SUPERVISED VISUAL PRE-TRAINING 11

MASKED FREQUENCY MODELING FOR SELF-SUPERVISED VISUAL PRE-TRA...

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11th International conference on Learning Representations, ICLR 2023

作者： Xie, Jiahao Li, Wei Zhan, Xiaohang Liu, Ziwei Ong, Yew Soon Loy, Chen Change S-Lab NTU Singapore SCSE NTU Singapore CUHK Hong Kong A*STAR Singapore

We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models. Instead of randomly inserting mask tokens to the input embeddings in the spatial domain, in this paper, we shift the perspective to the frequency domain. Specifically, MFM first masks out a portion of frequency components of the input image and then predicts the missing frequencies on the frequency spectrum. Our key insight is that predicting masked components in the frequency domain is more ideal to reveal underlying image patterns rather than predicting masked patches in the spatial domain, due to the heavy spatial redundancy. Our findings suggest that with the right configuration of mask-and-predict strategy, both the structural information within high-frequency components and the low-level statistics among low-frequency counterparts are useful in learning good representations. For the first time, MFM demonstrates that, for both ViT and CNN, a simple non-Siamese framework can learn meaningful representations even using none of the following: (i) extra data, (ii) extra model, (iii) mask token. Experimental results on image classification and semantic segmentation, as well as several robustness benchmarks show the competitive performance and advanced robustness of MFM compared with recent masked image modeling approaches. Furthermore, we also comprehensively investigate the effectiveness of classical image restoration tasks for representation learning from a unified frequency perspective and reveal their intriguing relations with our MFM approach. Project page: https://***/project/mfm/***. © 2023 11th International conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： image reconstruction

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