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

共 14809 条记录，以下是671-680 订阅

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Continuous-to-Continuous data Model vs. Discrete-to-Discrete data Model for the Statistical Iterative reconstruction Method 22nd

Continuous-to-Continuous Data Model vs. Discrete-to-Discrete...

引用

22nd Annual International conference on Computational Science (ICCS)

作者： Cierniak, Robert Czestochowa Tech Univ Dept Intelligent Comp Syst Armii Krajowej 36 PL-42200 Czestochowa Poland

ISBN: (纸本)9783031087547;9783031087530

The article presents a comparison of two statistical approaches to the problem of image reconstruction from projections: the worldwide known concept based on a discrete-to-discrete data model and our original idea based on a continuous-to-continuous data model. Both reconstruction approaches are formulated taking into account the statistical properties of signals obtained by CT scanners. The main goal of this strategy is significantly improving the quality of the reconstructed images, so allowing a reduction in the x-ray dose absorbed by a patient during CT examinations. In the concept proposed by us, the reconstruction problem is formulated as a shift-invariant system. In consequence, that significantly improves the quality of the subsequently reconstructed images, and it allows to reduce the computational complexity compared to the reference method. The performed by us experiments have shown that our original reconstruction method outperforms the referential approach regarding the image quality obtained and the time of necessary calculations.

关键词： Iterative reconstruction algorithms Computed tomography Statistical methods

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Viewset Diffusion: (0-)image-Conditioned 3D Generative Models from 2D data

Viewset Diffusion: (0-)Image-Conditioned 3D Generative Model...

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IEEE/CVF International conference on Computer Vision (ICCV)

作者： Szymanowicz, Stanislaw Rupprecht, Christian Vedaldi, Andrea Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9798350307184

We present Viewset Diffusion, a diffusion-based generator that outputs 3D objects while only using multi-view 2D data for supervision. We note that there exists a one-to-one mapping between viewsets, i.e., collections of several 2D views of an object, and 3D models. Hence, we train a diffusion model to generate viewsets, but design the neural network generator to reconstruct internally corresponding 3D models, thus generating those too. We fit a diffusion model to a large number of viewsets for a given category of objects. The resulting generator can be conditioned on zero, one or more input views. Conditioned on a single view, it performs 3D reconstruction accounting for the ambiguity of the task and allowing to sample multiple solutions compatible with the input. The model performs reconstruction efficiently, in a feed-forward manner, and is trained using only rendering losses using as few as three views per viewset. Project page: ***/viewset-diffusion.

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Robot Vision and Deep Learning for Automated Planogram Compliance in Retail 1

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4th International conference on Robotics, Computer Vision and Intelligent Systems (ROBOVIS)

作者： Merabet, Adel Latha, Abhishek V. Kuzhippallil, Francis A. Rahimipour, Mohammad Rhinelander, Jason Venkat, Ramesh St Marys Univ Div Engn Halifax NS B3N 3C3 Canada St Marys Univ Comp & Data Analyt Halifax NS B3N 3C3 Canada St Marys Univ Sobey Sch Business Halifax NS B3N 3C3 Canada

ISBN: (数字)9783031590573

ISBN: (纸本)9783031590566;9783031590573

In this paper, automated planogram compliance technique is proposed for retail applications. A mobile robot with camera vision capabilities provides the images of the products on shelves, which are processed to reconstruct an overall image of the shelves to be compared to the planogram. The image reconstruction includes image frames extraction from live video stream, images stitching and concatenation. Object detection, for the products, is achieved using a deep learning tool based on YOLOv5 model. dataset, for algorithm training and testing, is built to identify the products based on their image identification, number, and location on the shelf. A small scale of shelves with products is built and different cases of products on shelves are tested in a laboratory environment. It was found that YOLOv5 algorithm detects various products on shelves with a precision of 0.98, recall of 0.99, F-measure of 0.98, and clarification loss of 0.006.

关键词： Robot image Deep learning Object detection Planogram Retail

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Edge Structure Learning via Low Rank Residuals for Robust image Classification 37

Edge Structure Learning via Low Rank Residuals for Robust Im...

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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： Shen, Xiang-Jun Abhadiomhen, Stanley Ebhohimhen Yang, Yang Liu, Zhifeng Tian, Sirui JiangSu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Univ Nigeria Dept Comp Sci Nsukka Nigeria Nanjing Univ Sci & Technol Sch Elect & Opt Engn Dept Elect Engn Nanjing 210094 Peoples R China

ISBN: (纸本)9781577358800

Traditional low-rank methods overlook residuals as corruptions, but we discovered that low-rank residuals actually keep image edges together with corrupt components. Therefore, filtering out such structural information could hamper the discriminative details in images, especially in heavy corruptions. In order to address this limitation, this paper proposes a novel method named ESL-LRR, which preserves image edges by finding image projections from low-rank residuals. Specifically, our approach is built in a manifold learning framework where residuals are regarded as another view of image data. Edge preserved image projections are then pursued using a dynamic affinity graph regularization to capture the more accurate similarity between residuals while suppressing the influence of corrupt ones. With this adaptive approach, the proposed method can also find image intrinsic low-rank representation, and much discriminative edge preserved projections. As a result, a new classification strategy is introduced, aligning both modalities to enhance accuracy. Experiments are conducted on several benchmark image datasets, including MNIST, LFW, and COIL100. The results show that the proposed method has clear advantages over compared state-of-the-art (SOTA) methods, such as Low-Rank Embedding (LRE), Low-Rank Preserving Projection via Graph Regularized reconstruction (LRPP GRR), and Feature Selective Projection (FSP) with more than 2% improvement, particularly in corrupted cases.

关键词： image classification

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STYLEGAN-INDUCED data-DRIVEN REGULARIZATION FOR INVERSE PROBLEMS 47

STYLEGAN-INDUCED DATA-DRIVEN REGULARIZATION FOR INVERSE PROB...

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47th IEEE International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Conmy, Arthur Mukherjee, Subhadip Schonlieb, Carola-Bibiane Univ Cambridge Dept Appl Math & Theoret Phys Cambridge England

ISBN: (纸本)9781665405409

Recent advances in generative adversarial networks (GANs) have opened up the possibility of generating high-resolution photo-realistic images that were impossible to produce previously. The ability of GANs to sample from high-dimensional distributions has naturally motivated researchers to leverage their power for modeling the image prior in inverse problems. We extend this line of research by developing a Bayesian image reconstruction framework that utilizes the full potential of a pre-trained StyleGAN2 generator, which is the currently dominant GAN architecture, for constructing the prior distribution on the underlying image. Our proposed approach, which we refer to as learned Bayesian reconstruction with generative models (L-BRGM), entails joint optimization over the style-code and the input latent code, and enhances the expressive power of a pre-trained StyleGAN2 generator by allowing the style-codes to be different for different generator layers. Considering the inverse problems of image inpainting and super-resolution, we demonstrate that the proposed approach is competitive with, and sometimes superior to, state-of-the-art GAN-based image reconstruction methods.

关键词： Inverse problems generative prior

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Towards data-Driven Volatility Modeling with Variational Autoencoders 1

引用

European conference on Machine Learning and Principles and Practice of Knowledge Discovery in databases (ECML PKDD)

作者： Dierckx, Thomas Davis, Jesse Schoutens, Wim Katholieke Univ Leuven Dept Stat & Risk Leuven Belgium Katholieke Univ Leuven Dept Comp Sci Leuven Belgium

ISBN: (数字)9783031236334

ISBN: (纸本)9783031236327;9783031236334

In this study, we show how S&P 500 Index volatility surfaces can be modeled in a purely data-driven way using variational autoencoders. The approach autonomously learns concepts such as the volatility level, smile, and term structure without leaning on hypotheses from traditional volatility modeling techniques. In addition to introducing notable improvements to an existing variational autoencoder approach for the reconstruction of both complete and incomplete volatility surfaces, we showcase three practical use cases to highlight the relevance of this approach to the financial industry. First, we show how the latent space learned by the variational autoencoder can be used to produce synthetic yet realistic volatility surfaces. Second, we demonstrate how entire sequences of synthetic volatility surfaces can be generated to stress test and analyze an options portfolio. Third and last, we detect anomalous surfaces in our options dataset and pinpoint exactly which subareas are divergent.

关键词： Volatility modeling Variational autoencoder Synthetic data Market simulation Anomaly detection Portfolio stress testing

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image reconstruction based on Graph structure for MRI-guided diffuse optical tomography 17

Image reconstruction based on Graph structure for MRI-guided...

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17th International conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Zou, Peiwen Wei, Chengpu Hu, Ting Li, Zhe Sun, Zhonghua Jia, Kebin Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Diffuse optical tomography (DOT) is a non-invasive, label-free imaging technique widely used in applications such as breast cancer diagnosis and brain imaging. It allows for the quantitative measurement of tissue functional parameters, including the concentrations of oxyhemoglobin, deoxyhemoglobin, and water. However, the quality of reconstructed images is poor due to light scattering. To address this challenge, one effective strategy is to incorporate anatomical information from high-resolution imaging to guide DOT reconstruction. In this study, a new approach (GRI) is developed to leverage MRI images based on graph structure for DOT reconstruction. And its feasibility and effectiveness were evaluated with numerical simulations. The results indicate that GRI significantly improves the structural similarity index (SSIM) of reconstructed total hemoglobin (HbT) images, outperforming conventional Tikhonov regularization and direct regularization imaging (DRI) method by more than 63% and 82%, respectively. These findings are further supported by experiments using real patient data, which demonstrate GRI's potential in breast cancer diagnosis. © 2025 SPIE.

关键词： Optical tomography

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Two-Phase Pressure Drops Analysis in a Chevron-Type Corrugated Channel Using image Segmentation for Void Fraction Estimation 41

Two-Phase Pressure Drops Analysis in a Chevron-Type Corrugat...

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41st UIT International Heat Transfer conference

作者： Passoni, S. Ferrario, A. Mereu, R. Inzoli, F. Politecn Milan Dept Energy Via Lambruschini 4A I-20156 Milan Italy

This study examines the pressure drops and flow patterns in a Chevron-type Compact Plate Heat Exchanger (CPHE) with air-water two-phase mixture under adiabatic conditions. A transparent replica channel, made from epoxy resin, was used to mimic the heat exchanger's fluid dynamics and allow for optical access. The channel featured a 63 degrees corrugation angle, 2.5 mm depth, and 8.9 mm pitch, yielding a global enlargement factor of 1.17. A particular inlet section was designed to ensure even phase mixing and distribution. Operating conditions varied from 6 to 365 kg/m(2)s for water and 0.02 to 5 kg/m(2)s for air, expanding the range of previous studies. High-framerate back-illuminated visualizations provided detailed observations of the flow patterns. Moreover, frame-by-frame void fraction was estimated via HSV video analysis adopting a convolutional neural network (U-Net) to produce binary masks and estimate gas bubble volumes and via a volume-reconstruction algorithm exploiting the pseudo-2D shape of the channel. A novel correlation for Darcy's friction factor based on Reynolds number was developed for single-phase flow, complemented by an analysis of two-phase pressure drops using a two-phase multiplier correlated with the Lockhart-Martinelli parameter. The incorporation of void fraction data significantly improved their modelling accuracy.

关键词： Reynolds number

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A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric image reconstruction 26

A Conditional Denoising Diffusion Probabilistic Model for Ra...

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26th European conference on Artificial Intelligence, ECAI 2023

作者： Wang, Ruoqi Chen, Zhuoyang Luo, Qiong Wang, Feng China Guangzhou University China The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781643684369

In radio astronomy, signals from radio telescopes are transformed into images of observed celestial objects, or sources. However, these images, called dirty images, contain real sources as well as artifacts due to signal sparsity and other factors. Therefore, radio interferometric image reconstruction is performed on dirty images, aiming to produce clean images in which artifacts are reduced and real sources are recovered. So far, existing methods have limited success on recovering faint sources, preserving detailed structures, and eliminating artifacts. In this paper, we present VIC-DDPM, a Visibility and image Conditioned Denoising Diffusion Probabilistic Model. Our main idea is to use both the original visibility data in the spectral domain and dirty images in the spatial domain to guide the image generation process with DDPM. This way, we can leverage DDPM to generate fine details and eliminate noise, while utilizing visibility data to separate signals from noise and retaining spatial information in dirty images. We have conducted experiments in comparison with both traditional methods and recent deep learning based approaches. Our results show that our method significantly improves the resulting images by reducing artifacts, preserving fine details, and recovering dim sources. This advancement further facilitates radio astronomical data analysis tasks on celestial phenomena. Our code is available at https://***/RapidsAtHKUST/VIC-DDPM. © 2023 The Authors.

关键词： Visibility

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Elevating Perception: Unified Recognition Framework and Vision-Language Pre-Training Using Three-Dimensional image reconstruction 2

Elevating Perception: Unified Recognition Framework and Visi...

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2nd International conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Wang, ZhiQiang Joshi, Aryan Zhang, GuiYing Ren, WenJia Jia, Feng Sun, XiaoHang Tianjin University of Technology and Education Dept. of Information Tech & Engineering Tianjin China Tianjin Ai Shen de Si Technology Co. Ltd Dept. of Information Tech & Engineering Tianjin China

ISBN: (纸本)9798350360363

This research project explores a paradigm shift in perceptual enhancement by integrating a Unified Recognition Framework and Vision-Language Pre-Training in three-dimensional image reconstruction. Through the synergy of advanced algorithms from computer vision & language processing, the project tries to enhance the precision and depth of perception in reconstructed images. This innovative approach holds the potential to revolutionize fields such as medical imaging, virtual reality, and computer-aided design, providing a comprehensive perspective on the intersection of multimodal data processing and perceptual advancement. The anticipated research outcomes are expected to significantly contribute to the evolution of technologies that rely on accurate and contextually rich three-dimensional reconstructions. Moreover, the research aims to reduce the constant need for new datasets by improving pattern recognition through 3D image patterning on backpropagation. This continuous improvement of vectors is envisioned to enhance the efficiency and accuracy of pattern recognition, contributing to the optimization of perceptual systems over time. © 2023 IEEE.

关键词： image enhancement

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