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

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An Accurate Attenuation and Scatter Correction Method for PET/MR Scanners with Lutetium-based Scintillators

An Accurate Attenuation and Scatter Correction Method for PE...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： M. Teimoorisichani H. Sari J. Hamill v. Panin P. Schleyer A. Rominger M. Conti Siemens Medical Solutions USA Inc Knoxville Tennessee United States of America Siemens Healthcare AG Advanced Clinical Imaging Technology Lausanne Switzerland Department of Nuclear Medicine Inselspital Bern University Hospital Bern Switzerland Department of Nuclear Medicine University of Bern Bern Switzerland

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

A novel method for attenuation and scatter correction of PET data in PET/MR is investigated in this work. Given the absence of an accurate attenuation map in PET/MR, the use of an activity and attenuation joint reconstruction algorithm such as TOF-MLAA is studied. To address issues in TOF-MLAA, a number of, improvements were investigated: 1) the use of background radiation from lutetium-based scintillators as a weak transmission source, 2) denoising attenuation maps with artificial intelligence, and 3) the use of an energy-based scatter method that does not require any prior anatomical knowledge. The proposed method is evaluated on PET data from a Siemens Biograph vision Quadra scanner using phantom and patient data. images of NEMA NU-2 image quality showed that the proposed method retains the contrast recovery and background variability of the PET images within 5% of the reference CT-based reconstructed PET images. Similarly, organ uptake in PET images using the proposed method showed an average of 5% error. In conclusions, the proposed reconstruction framework showed promising results towards quantitative CT-less PET suitable for PET/MR.

关键词： Microwave integrated circuits Scintillators Accuracy Semiconductor detectors Noise reduction Phantoms reconstruction algorithms

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Detection of Fish Species Captured by Artisanal Canoes Using YOLOv11

Detection of Fish Species Captured by Artisanal Canoes Using...

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Intelligent Computing and Next Generation Networks（ICNGN), International conference on

作者： Marc Momar Tall Ibrahima Ngom Ousmane Sadio Ibrahima Diagne Adama Coulibaly Moustapha Ndiaye Polytechnic Higher School Cheikh Anta Diop University Dakar Senegale

ISBN: (数字)9798331529222

ISBN: (纸本)9798331529239

In Senegal, fish are caught and classified manually, and declarations are often incomplete. data collection at fishing wharves on the Senegalese coast is limited by the lack of suitable tools for identifying and effectively recording catches. Although studies have been carried out to improve management in the fisheries sector, there is a notable lack of local databases and automated systems for detecting and counting fish. This article proposes a detection and identification model, which should contribute to the creation of a suitable local database. The data used are images taken on target docks and the data are enriched with information from external sources. These data were used to produce a segmented, structured set of views. The database is used with YOLO v8 and YOLO v11 to improve the model. image detection using bounding boxes is a key step in training the model. It is trained a priori by YOLO v8, which produced recall and confidence scores ranging from 0.01 to 0.75. These results are enhanced in this paper by the YOLO v11 model. very efficient for automatic detection and counting, this model shows promising performance for simulations. Metrics such as mAP50 (with an IoU threshold of 0.5) and mAP50-95 (average over several thresholds) show a clear evolution, indicating a significant progress in the detection capacity of the model. These results highlight the accuracy and efficiency of YOLO v11 in identifying and positioning bounding boxes.

关键词： YOLO Training databases Semantic segmentation Refining Manuals data collection Fisheries Recording Next generation networking

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Learning-by-Synthesis for Appearance-based 3D Gaze Estimation

Learning-by-Synthesis for Appearance-based 3D Gaze Estimatio...

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IEEE conference on Computer vision and Pattern Recognition

作者： Yusuke Sugano Yasuyuki Matsushita Yoichi Sato The University of Tokyo Microsoft Research Asia

ISBN: (纸本)9781479951192

Inferring human gaze from low-resolution eye images is still a challenging task despite its practical importance in many application scenarios. This paper presents a learning-by-synthesis approach to accurate image-based gaze estimation that is person- and head pose-independent. Unlike existing appearance-based methods that assume person-specific training data, we use a large amount of cross-subject training data to train a 3D gaze estimator. We collect the largest and fully calibrated multi-view gaze dataset and perform a 3D reconstruction in order to generate dense training data of eye images. By using the synthesized dataset to learn a random regression forest, we show that our method outperforms existing methods that use low-resolution eye images.

关键词： Head Three-dimensional displays Estimation Cameras Training Training data vegetation

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Tomography Methods in Measuring of Optical Radiation Intensity Distribution by Grid Receivers

Tomography Methods in Measuring of Optical Radiation Intensi...

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International conference/Workshop on Laser and Fiber-Optical Networks Modeling (LFNM)

作者： v.M. Artemiev A.O. Naumov N.G. Kokodiy v.A. Timanyuk P. Kohns Institute of Applied Physics National Academy of Science of Belarus Minsk Belarus National University of Pharmacy Kharkiv Ukraine Bonn Germany

The method of processing of data from optical radiation grid receiver for measuring of intensity distribution is proposed. Algorithm of tomography is used in the method. Mathematical modeling of processes in receiver ... 详细信息

关键词： Optical receivers Tomography Nonlinear optics Equations image reconstruction Electromagnetic measurements Testing Electromagnetic radiation Wire Optical filters

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Wavelength Independent image Classification Through A Multimode Fiber Using Deep Neural Networks

Wavelength Independent Image Classification Through A Multim...

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conference on Lasers and Electro-Optics Europe

作者： Eirini Kakkava Navid Borhani Babak Rahmani Ugur Tegin Christophe Moser Demetri Psaltis Optics Laboratory École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland Laboratory of Applied Photonic Devices École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland

ISBN: (纸本)9781728104690

Deep Neural Networks (DNNs) have been increasingly implemented in different research fields or industrial applications. Large amounts of data are processed daily in order to extract useful information using machine learning techniques. Many research groups have shown impressive results on improving resolution in microscopy and quantitative phase retrieval by training DNNs on real datasets. Recently, recovery and reconstruction of images after they have propagated through multimode optical fibers (MMFs) have also been achieved using DNNs. When images propagate through MMFs they suffer severe scrambling because the information gets distributed among the different spatial modes that the fiber supports. Furthermore, since the fiber modes propagate with different velocities, the local information of the input decorrelates after a few millimeters along the MMF, thus resulting in the formation of a speckle pattern at the output. Recovery of information from such speckle patterns is of practical interest for integrating the MMFs for endoscopic applications in medicine or for signal recovery in telecommunications.

关键词： image reconstruction Laser sources Multimode fibers Neural networks Phase retrieval Speckle patterns

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Self-Guided and MR-Guided Deep-Learned Post-reconstruction PET Processing

Self-Guided and MR-Guided Deep-Learned Post-Reconstruction P...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Guillaume Corda-D’Incan Julia A. Schnabel Andrew J. Reader School of Biomedical Engineering and Imaging Sciences King’s College London UK The Technical University of Munich Germany The Helmholtz Center Munich Germany

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Reconstructed PET images exhibit high noise levels and low spatial resolution when shorter scan times and reduced injected doses are used. Regularisation methods such as post-reconstruction smoothing can help to improve image quality. Recently, neural networks have proved to be highly effective for this task by learning an ensemble of kernels. For post-processing of PET images, a high-resolution MR image can also be used for guidance to further improve the final image quality. In this work, we investigate the impact of the input choice and the number of training samples used on a neural network’s performance for PET post-reconstruction processing. To do so, six combinations of low-count PET and MR independent reconstruction outputs are fed into a state-of-the-art residual convolutional neural network (CNN). Six different networks were trained using as input i) the last iteration of a conventional PET reconstruction, ii) all the iterates from the PET reconstruction, iii) only the final PET and MR estimates, iv) all the PET estimates and the final MR, v) the final PET and all the MR estimates and vi) all the iteration outputs of independent PET and MR reconstruction. The networks have been trained using a different number of training samples as well. The results obtained suggest that using all the intermediate reconstructions lead the network to perform better when the training set size is limited. Furthermore, the gain in performance observed when the dataset size increases are higher for methods using all the intermediate reconstruction outputs. Future work will focus on training networks with a higher number of training samples to confirm the trend observed and assess the proposed method on 3D real data.

关键词： Training image quality Three-dimensional displays Smoothing methods Neural networks Convolutional neural networks Task analysis

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A noise equivalent counts approach for optimizing I-124 imaging performance with a small animal PET scanner

A noise equivalent counts approach for optimizing I-124 imag...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Qinan Bao v. Kohli F.R. Rannou A.F. Chatziioannou Crump Institute for Molecular Imaging David Geffen School of Medicine UCLA Los Angeles CA USA PET Department The Queen's Medical Center Honolulu HI USA Departmento de Ingenieria Informatica Universidad de Santiago de Chile

Due to its long radiological half life, 124 I can be used for investigation of lengthy biological processes. However, it is not an ideal PET isotope because it has poor positron yield and half of the positrons are emitted in cascade with 603 kev gammas. This decay spectrum results in coincidences between gammas and annihilation photons (dirty coincidences) leading to both inaccurate quantitation and overall reduction in the image quality. It has been shown that noise equivalent counts (NEC) have a direct link to signal to noise ratio of the reconstructed images. The purpose of this work was to investigate the effect of varying the energy window of the digitizer on the NEC measured from the non-pure positron emitter 124 I and hence, on the image quality. Simulations were performed using the GATE software for a 1.32 cm radius cylinder filled with 300 muCi of 124 I placed at the center of field of view. We report that by windowing in on the upper energy window of the data acquisition from a symmetric 50% (255-766 kev) energy window, to an asymmetric 255-600 kev one, it is possible to preferentially reduce the dirty coincidences with minor sensitivity changes. These findings will be verified with experimental data, and will lead to optimizing the data acquisition chain for 124 I imaging performance

关键词： Animals Positron emission tomography image quality Signal to noise ratio National electric code data acquisition Biological processes Isotopes image reconstruction Energy measurement

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DublinCity: Annotated LiDAR point cloud and its applications 30

DublinCity: Annotated LiDAR point cloud and its applications

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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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Dense Object reconstruction with Semantic Priors

Dense Object Reconstruction with Semantic Priors

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IEEE conference on Computer vision and Pattern Recognition

作者： Sid Yingze Bao Manmohan Chandraker Yuanqing Lin Silvio Savarese University of Michigan at Ann Arbor NEC Labs America

ISBN: (纸本)9781467364102

We present a dense reconstruction approach that overcomes the drawbacks of traditional multiview stereo by incorporating semantic information in the form of learned category-level shape priors and object detection. Given training data comprised of 3D scans and images of objects from various viewpoints, we learn a prior comprised of a mean shape and a set of weighted anchor points. The former captures the commonality of shapes across the category, while the latter encodes similarities between instances in the form of appearance and spatial consistency. We propose robust algorithms to match anchor points across instances that enable learning a mean shape for the category, even with large shape variations across instances. We model the shape of an object instance as a warped version of the category mean, along with instance-specific details. Given multiple images of an unseen instance, we collate information from 2D object detectors to align the structure from motion point cloud with the mean shape, which is subsequently warped and refined to approach the actual shape. Extensive experiments demonstrate that our model is general enough to learn semantic priors for different object categories, yet powerful enough to reconstruct individual shapes with large variations. Qualitative and quantitative evaluations show that our framework can produce more accurate reconstructions than alternative state-of-the-art multiview stereo systems.

关键词： Shape Three-dimensional displays image reconstruction Semantics Training Silicon Object detection

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A method for assessing nonlinear growth in the fetal cortex

A method for assessing nonlinear growth in the fetal cortex

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Annual International conference of the IEEE Engineering in Medicine and Biology Society

作者： Rosita Shishegar Joanne M. Britto Leigh A. Johnston NeuroEngineering Laboratory Department of Electrical and Electronic Engineering University of Melbourne Florey Institute of Neuroscience and Mental Health

ISBN: (纸本)9781424479276

The cerebral cortex is folded into gyri and sulci in the brains of higher mammals. Quantitative study of the process by which the cortex folds during brain development is critical to a complete understanding of normal brain development and neuro-developmental disorders. In this work, we propose a new method by which to localise nonlinearities in the cortical folding process, and thereby identify regions of differential growth across the cortex. Our method is based on spherical harmonic (SPHARM) representation of the cortical surface. Linearity is assessed by comparison of each SPHARM reconstructed surface with an artificial surface constructed using a linear combination of SPHARM coefficients from data at adjoining developmental time points. The resultant quantification of cortical folding development is easy to interpret, and the method has low computational cost. We demonstrate application to a set of experimental MRI data of fetal sheep brains, across key developmental timepoints as the cortex first folds during development.

关键词： Surface reconstruction Shape image reconstruction Approximation methods Harmonic analysis Surface morphology Complexity theory

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