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

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Semi-Supervised Learning for Low-Dose CT image Restoration with Hierarchical Deep Generative Adversarial Network (HD-GAN) 41

Semi-Supervised Learning for Low-Dose CT Image Restoration w...

引用

41st Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Choi, Kihwan vania, Malinda Kim, Sungwon Korea Inst Sci & Technol Ctr Bion Seoul 02792 South Korea Yonsei Univ Dept Radiol Coll Med Seoul 03722 South Korea

ISBN: (纸本)9781538613115

In the absence of duplicate high-dose CT data, it is challenging to restore high-quality images based on deep learning with only low-dose CT (LDCT) data. When different reconstruction algorithms and settings are adopted to prepare high-quality images, LDCT datasets for deep learning can be unpaired. To address this problem, we propose hierarchical deep generative adversarial networks (HD-GANs) for semi-supervised learning with the unpaired datasets. We first cluster each patient's CT images into multiple categories, and then collect the images in the same categories across different patients to build an imageset for denoising. Each imageset is fed into a generative adversarial network that consists of a denoising network and a following classification network. The denoising network efficiently reuses feature maps from the lower layers for end-to-end learning with full-size images. The classifier is trained to distinguish between the denoised images and the high-quality images. Evaluated with a clinical LDCT dataset, the proposed semi-supervised learning approach efficiently reduces the noise level of LDCT images without loss of information, thereby addressing the major shortcomings of IR such as computation time and anatomical inaccuracy.

关键词： Computed tomography Noise reduction image reconstruction Training Generative adversarial networks Noise measurement Gallium nitride

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Fast image reconstruction in Ultrasound Transmission Tomography by U-net

Fast Image Reconstruction in Ultrasound Transmission Tomogra...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Hongjian Wang Xueze Qian Hartmut Gemmeke Torsten Hopp Nicole v. Ruiter Jürgen Hesser School of Computer Science and Technology Donghua University Shanghai China Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Medical Faculty Mannheim Heidelberg University Mannheim Germany

Conventional iterative methods for image reconstruction in ultrasound transmission tomography need to perform many iterations where at each iteration one has to compute the complex forward model of ultrasound wave propagation, and hence they are time-consuming. We use a U-net neural network to accelerate the reconstruction, by training the network to map from an initial reconstruction obtained via a few iterations of L-BFGS method to the target ground truth image. Since the computation of a forward pass of the neural network is very fast, we can expect a significant acceleration using the trained network for image reconstruction. Experiments show that our trained network can replace 40 L-BFGS iterations to generate equivalent reconstructions with slightly better quantitative quality in terms of normalized root mean square error and better visual quality due to the network's denoising effect. It can achieve up to 283× speedup compared with L-BFGS method for reconstructing small-size sound speed images with 80×80 pixels. This implies that we can expect even greater acceleration effects when applying such approach to reconstruct large-size 3D images.

关键词： Training visualization Ultrasonic imaging Three-dimensional displays Propagation Neural networks Noise reduction

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WAvELENGTH-RESOLvED NEUTRON TOMOGRAPHY FOR CRYSTALLINE MATERIALS 44

WAVELENGTH-RESOLVED NEUTRON TOMOGRAPHY FOR CRYSTALLINE MATER...

引用

44th IEEE International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： venkatakrishnan, S. v. Dessieux, Luc Bingham, Philip Oak Ridge Natl Lab Imaging Signals & Machine Learning Grp Oak Ridge TN 37831 USA Univ Tennessee Dept Phys Knoxville TN 37931 USA

ISBN: (纸本)9781479981311

Wavelength-resolved (WR) neutron transmission tomography is an emerging technique to characterize engineering materials. While tomographic reconstruction for amorphous samples is straightforward, it is challenging to reconstruct samples with single-crystal domains because the attenuation of the sample varies as a function of its orientation with respect to the incident beam due to Bragg scattering. In this paper, we present an algorithm that can reconstruct samples with single-crystal domains from WR neutron tomographic measurements. In particular, we use a model-based iterative reconstruction (MBIR) technique that reconstructs the volume by identifying and leaving out the regions of the measurement that are affected by Bragg scatter. We combine the output of the MBIR method with an algorithm that matches the reconstruction to the identified Bragg scatter to reconstruct a feature that corresponds to the local crystallography of the sample being measured. Using simulated data, we demonstrate how our algorithm can reconstruct materials with single-crystal domains, thereby adding a powerful new capability for WR neutron imaging instruments.

关键词： image reconstruction Tomography Attenuation Neutrons Three-dimensional displays Wavelength measurement

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DISTANCE-TRAINING FOR image-BASED 3D MODELLING OF ARCHEOLOGICAL SITES IN REMOTE REGIONS 2

DISTANCE-TRAINING FOR IMAGE-BASED 3D MODELLING OF ARCHEOLOGI...

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2nd International conference of Geomatics and Restoration (GEORES)

作者： Yordanov, v Mostafavi, A. Scaioni, M. Vasil Levski Natl Mil Univ Veliko Tamovo Bulgaria Politecn Milan Dept Civil & Environm Engn DICA Piazza Leonardo da Vinci 32 Milan Italy Node Off Tehran Tehran Iran Politecn Milan Dept Architecture Built Environm & Construct Engn Via Ponzio 31 Milan Italy

The impressive success of Structure-from-Motion Photogrammetry (SfM) has spread out the application of image-based 3D reconstruction to a larger community. In the field of Archeological Heritage documentation, this has opened the possibility of training local people to accomplish photogrammetric data acquisition in those remote regions where the organization of 3D surveying missions from outside may be difficult, costly or even impossible. On one side, SfM along with low-cost cameras makes this solution viable. On the other, the achievement of high-quality photogrammetric outputs requires a correct image acquisition stage, being this the only stage that necessarily has to be accomplished locally. This paper starts from the analysis of the well-know "3x3 Rules" proposed in 1994 when photogrammetry with amateur camera was the state-of-the art approach and revises those guidelines to adapt to SfM. Three aspects of data acquisition are considered: geometry (control information, photogrammetric network), imaging (camera/lens selection and setup, illumination), and organization. These guidelines are compared to a real case study focused on Ziggurat Chogha Zanbil (Iran), where four blocks from ground stations and drone were collected with the purpose of 3D modelling.

关键词： Archeology Photogrammetry Remote Areas Structure-from-Motion Training

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Perceptually Driven Conditional GAN for Fourier Ptychography 53

Perceptually Driven Conditional GAN for Fourier Ptychography

引用

53rd Asilomar conference on Signals, Systems, and Computers (ACSSC)

作者： Kumar, Abhinau, v Gupta, Shashank Channappayya, Sumohana S. Indian Inst Technol Dept Elect Engn Hyderabad India

ISBN: (纸本)9781728143002

Fourier Ptychography (FP) is a computational imaging technique which artificially increases the effective numerical aperture of an imaging system. In FP, the object is imaged using an array of Light Emitting Diodes (LEDs), each from a different illumination angle. A high resolution image is synthesized from this low resolution stack, typically using iterative phase retrieval algorithms. However, such algorithms are time consuming and fail when the overlap between the spectra of images is low, leading to high data requirements. At the crux of FP lies a phase retrieval problem. In this paper, we propose a Deep Learning (DL) algorithm to perform this synthesis under low spectral overlap between samples, and show a significant improvement in phase reconstruction over existing DL algorithms.

关键词： Fourier Ptychography Phase Retrieval Conditional Generative Adversarial Networks Deep Learning

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Experimental analysis of ray-based sound speed reconstruction algorithms for phase aberration corrected USCT SAFT imaging

Experimental analysis of ray-based sound speed reconstructio...

引用

conference on Medical Imaging - Ultrasonic Imaging and Tomography

作者： Hopp, T. Zuch, F. Zapf, M. Gemmeke, H. Ruiter, N., v Karlsruhe Inst Technol KIT Karlsruhe Germany

ISBN: (数字)9781510625587

ISBN: (纸本)9781510625587

For Ultrasound Tomography reflectivity imaging Synthetic Aperture Focusing Technique (SAFT) is often applied. Phase aberration correction is required to achieve images with high resolution and high contrast, for which a sound speed map is required. For USCT systems these sound speed maps are usually reconstructed using the transmission data from the raw data set, which is also used for reflection tomography. We compare straight and bent ray phase aberration correction SAFT algorithms with respect to different reconstruction algorithms to derive the sound speed map. Evaluations are carried out based on a simulated phantom and measured data from the Multimodal Ultrasound Breast Imaging System (MUBI). Phase aberration correction enables recovering the contrast of the image, while without SAFT results in considerably unfocused inner structures. By applying a reconstructed sound speed map however the local contrast cannot be fully recovered compared to the ideal case. Introducing bent ray transmission reconstruction approaches based on the Fast Marching or Bezier curve method in all cases improves the results over the straight ray transmission tomography.

关键词： Ultrasound computer tomography breast imaging Synthetic Aperture Focusing Technique Phase aberration correction Ultrasound transmission imaging

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DublinCity: Annotated LiDAR point cloud and its applications 30

DublinCity: Annotated LiDAR point cloud and its applications

引用

30th British Machine vision conference, BMvC 2019

作者： Iman Zolanvari, S.M. Ruano, Susana Rana, Aakanksha Cummins, Alan Da Silva, Rogerio Eduardo Rahbar, Morteza Smolic, Aljosa V-SENSE School of Computer Science and Statistics Trinity College Dublin Ireland University of Houston-Victoria VictoriaTX United States CAAD Department of Architecture ETH Zurich Switzerland Department of Architecture Tarbiat Modares University Tehran Iran

Scene understanding of full-scale 3D models of an urban area remains a challenging task. While advanced computer vision techniques offer cost-effective approaches to analyse 3D urban elements, a precise and densely labelled dataset is quintessential. The paper presents the first-ever labelled dataset for a highly dense Aerial Laser Scanning (ALS) point cloud at city-scale. This work introduces a novel benchmark dataset that includes a manually annotated point cloud for over 260 million laser scanning points into 100'000 (approx.) assets from Dublin LiDAR point cloud [12] in 2015. Objects are labelled into 13 classes using hierarchical levels of detail from large (i.e. building, vegetation and ground) to refined (i.e. window, door and tree) elements. To validate the performance of our dataset, two different applications are showcased. Firstly, the labelled point cloud is employed for training Convolutional Neural Networks (CNNs) to classify urban elements. The dataset is tested on the well-known state-of-the-art CNNs (i.e. PointNet, PointNet++ and So-Net). Secondly, the complete ALS dataset is applied as detailed ground truth for city-scale image-based 3D reconstruction. © 2019. The copyright of this document resides with its authors.

关键词： Antennas

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Adaptive distributed video surveillance system

Adaptive distributed video surveillance system

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Technology and Entrepreneurship, (ICTE-v), International conference on

作者： Alexander Khelvas Darya Demyanova Alexander Gilya-Zetinov Egor Konyagin Roman Khafizov Ruslan Pashkov Center of Open Systems MIPT Dolgoprudnii Russian Federation Center of Open Systems Skoltech Moscow Russian Federation

ISBN: (数字)9781728193748

ISBN: (纸本)9781728193755

video surveillance systems are a major multipurpose video data source for modern emergency management solutions. As their complexity and image quality grows, the requirements for data exchange channels, storage and processing units are becoming much more ***, a wide range of neural networks becomes a universal instrument for image analysis. Such algorithms require deployment of powerful HPC-class server *** this article, we propose a new adaptive approach to decentralized environment for video data storage and event-driven video processing for modern emergency management *** proposed solutions have been tested on different arrays of video data collected from various sources. These sources include city video surveillance systems, supermarket video surveillance systems, *** each of the listed examples, a discrete dynamic model has been developed based on video processing and event *** research resulted in a new data collection and analysis approach based on distributed video processing, reconstruction of 3D scenes and tactical situations detection.

关键词： video stream neural network artificial intelligence smart camera skeleton body model Smart cameras Neural network Artificial Intelligence video monitoring video data video stream SURvEILLANCE SYSTEMS data exchange crisis management

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An Iteration Method for X-Ray CT reconstruction from variable-Truncation Projection data 7th

An Iteration Method for X-Ray CT Reconstruction from Variabl...

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7th International conference on Scale Space and variational Methods in Computer vision (SSvM)

作者： Huo, Limei Luo, Shousheng Dong, Yiqiu Tai, Xue-Cheng Wang, Yang Henan Univ Sch Math & Stat Data Anal Technol Lab Kaifeng 475001 Henan Peoples R China Beijing Computat Sci Res Ctr Beijing 100193 Peoples R China Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark Hong Kong Baptist Univ Dept Math Kowloon Tong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Math Kowloon Clear Water Bay Hong Kong Peoples R China

ISBN: (纸本)9783030223687;9783030223670

In this paper, we investigate the in-situ X-ray CT reconstruction from occluded projection data. For each X-ray beam, we propose a method to determine whether it passes through a measured object by comparing the observed data before and after the measured object is placed. Therefore, we can obtain a prior knowledge of the object, that is some points belonging to the background, from the X-ray beam paths that do not pass through the object. We incorporate this prior knowledge into the sparse representation method for in-situ X-ray CT reconstruction from occluded projection data. In addition, the regularization parameter can be determined easily using the artifact severity estimation on the identified background points. Numerical experiments on simulated data with different noise levels are conducted to verify the effectiveness of the proposed method.

关键词： In-situ X-ray CT Occluded projection data Sparse representation Noise estimation

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Partially Occluded Hands: A Challenging New dataset for Single-image Hand Pose Estimation 14th

Partially Occluded Hands: A Challenging New Dataset for Sing...

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14th Asian conference on Computer vision (ACCv)

作者： Myanganbayar, Battushig Mata, Cristina Dekel, Gil Katz, Boris Ben-Yosef, Guy Barbu, Andrei MIT CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9783030208721;9783030208738

Recognizing the pose of hands matters most when hands are interacting with other objects. To understand how well both machines and humans perform on single-image 2D hand-pose reconstruction from RGB images, we collected a challenging dataset of hands interacting with 148 objects. We used a novel methodology that provides the same hand in the same pose both with the object being present and occluding the hand and without the object occluding the hand. Additionally, we collected a wide range of grasps for each object designing the data collection methodology to ensure this diversity. Using this dataset we measured the performance of two state-of-the-art hand-pose recognition methods showing that both are extremely brittle when faced with even light occlusion from an object. This is not evident in previous datasets because they often avoid hand-object occlusions and because they are collected from videos where hands are often between objects and mostly unoccluded. We annotated a subset of the dataset and used that to show that humans are robust with respect to occlusion, and also to characterize human hand perception, the space of grasps that seem to be considered, and the accuracy of reconstructing occluded portions of hands. We expect that such data will be of interest to both the vision community for developing more robust hand-pose algorithms and to the robotic grasp planning community for learning such grasps.

关键词： Partial occlusion dataset RGB hand-pose reconstruction

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