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检索条件"任意字段=2nd Conference on Image Reconstruction from Incomplete Data"

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A GEOGRAPHIC data GATHERING SYSTEM FOR image GEOLOCALIZATION REFINING 2

A GEOGRAPHIC DATA GATHERING SYSTEM FOR IMAGE GEOLOCALIZATION...

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2nd International conference on Smart data and Smart Cities

作者： Semaan, B. Servieres, M. Moreau, G. Chebaro, B. UMR 1563 AAU CRENAU Nantes France Lebanese Univ Beirut Lebanon

image geolocalization has become an important research field during the last decade. This field is divided into two main sections. The first is image geolocalization that is used to find out which country, region or city the image belongs to. The second one is refining image localization for uses that require more accuracy such as augmented reality and three dimensional environment reconstruction using images. In this paper we present a processing chain that gathers geographic data from several sources in order to deliver a better geolocalization than the GPS one of an image and precise camera pose parameters. In order to do so, we use multiple types of data. Among this information some are visible in the image and are extracted using image processing, other types of data can be extracted from image file headers or online image sharing platforms related information. Extracted information elements will not be expressive enough if they remain disconnected. We show that grouping these information elements helps finding the best geolocalization of the image.

关键词： image Geolocalization Refining Building Detection Semantic data Extraction Geographic data Collection Multi-Source data Analysis

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Adaptive Recovery of incomplete datasets for Edge Analytics 2

Adaptive Recovery of Incomplete Datasets for Edge Analytics

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2nd IEEE International conference on Fog and Edge Computing (ICFEC)

作者： Lujic, Ivan De Maio, Vincenzo Brandic, Ivona Vienna Univ Technol Inst Informat Syst Engn Favoritenstr 9-11-194 A-1040 Vienna Austria

ISBN: (纸本)9781538664889

The Internet of Things (IoT) has attracted significant attention from both academia and industry, thanks to applications such as smart cities, smart buildings and intelligent traffic management. These systems rely on data, collected from IoT devices, that are sent to the cloud for analytics. data are either used for near real-time decisions or stored for long-term analysis. However, in highly distributed IoT systems, missing or invalid data may appear because of different reasons including sensor failures, monitoring system failures and network failures. Analyzing incomplete datasets can lead to inaccurate results and imprecise decisions, with negative effects on the target systems. Also, due to the increasing size of such systems and the consequently increasing amount of data generated from sensors, recovery of incomplete datasets for analytics on the cloud is often infeasible, due to the limited bandwidth available and the strict latency constraints of IoT applications. We propose a novel semi-automatic recursive mechanism for recovery of incomplete datasets on the edge that is closer to the source of data. This mechanism enables efficient recovery of incomplete datasets employing different forecasting techniques for multiple gaps, based on user specifications. We evaluate our approach on datasets coming from the context of smart buildings and smart homes. The experimental results show that our approach is able to identify multiple gaps, then recover incomplete datasets, decreasing forecasting error by up to 82.68%, and reducing running time by up to 52.38%.

关键词： Edge Computing Internet of Things time series incomplete data data recovery forecasting techniques

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Continuous Surface Fitting to Spatial Optical Interferometer data

Continuous Surface Fitting to Spatial Optical Interferometer...

引用

conference on image reconstruction from incomplete data VI

作者： Rasheed, Sarbast Hajian, Arsen R. Tycner, Christopher Vu, Linda Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Cent Michigan Univ Dept Phys Mt Pleasant MI 48859 USA

ISBN: (纸本)9780819482969

In this paper, we describe a practical implementation of an image reconstruction method designed to generate a map of the brightness distribution from data consisting of squared visibilities and complex closure amplitudes resulting from observations of an astronomical target with a broadband, multichannel, spatial optical interferometer. Given the data, the method estimates the true brightness distribution with a model sampled on a rectangular grid of discrete positions on the sky with the assumption that the model intensities in the region not defined by the discrete positions being described by bilinear interpolation of the discrete intensities. The developed image reconstruction method has been applied to real observational data obtained from existing optical interferometer facilities.

关键词： image reconstruction modelling optical interferometry optimization

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Compressive Sensing approach for data Recovery from incomplete Measurements for One dimensional Signal 2

Compressive Sensing approach for Data Recovery from Incomple...

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2nd International conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

作者： Parate, Pratik Kanjalkar, Pramod Vishwakarma Inst Technol Instrumentat Engn Dept Pune Maharashtra India KL Univ Dept ECE Guntur India

ISBN: (纸本)9781509023998

In various situations there is a need to measure and transmit fast moving data such as in Radar, Oceanography, Continuous Monitoring of Meteorological Parameters, etc. It may sometimes happen that some data gets lost. So to recover this information complete original data is to be resent. In this paper, we have discussed a new type of signal acquisition theory called as Compressive Sensing (CS). Signals having Sparse representation in one or the other domain can be faithfully reconstructed from the random undersampled measurements. Naturally occurring signals such as audio signals are having sparse nature in Fourier Domain. One such implementation of CS theory is also explained and demonstrated. Greedy Algorithm known as Orthogonal Matching Pursuit is used in recovery process.

关键词： Compressive Sensing (CS) Sparsity RIP OMP

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A systematic algorithm for 3-D reconstruction of MRI based brain tumors using morphological operators and bicubic interpolation

A systematic algorithm for 3-D reconstruction of MRI based b...

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2010 2nd International conference on Computer Technology and Development, ICCTD 2010

作者： Amruta, Anantatamukala Gole, Abhijeet Karunakar, Yogesh Ramnarain Ruia College University of Mumbai India

ISBN: (纸本)9781424488438

This paper presents a method for 3D image reconstruction, which is one of the most attractive avenues in digital image processing techniques, especially due to its application in biomedical imaging. The diversity and complexity of tumor cells makes it very challenging to visualize tumor present in magnetic resonance image (MRI) data. This paper involves implementing various steps of extracting the tumor from the 2D slices of MRI brain images by OTSUs threshold technique and various morphological operations and designing software for reconstructing 3D image from a set of 2D tumor images. Extensive use of custom made user interface that provides for ease of user interaction and visualization of reconstructed data. The volume of the tumor is also estimated based on the computation of these images. Doctors and Radiologists can now prepare and image thousands of samples and save on time per day using this automation. © 2010 IEEE.

关键词： image reconstruction

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image Fusion-based Multi-frequency Microwave Tomography 2

Image Fusion-based Multi-frequency Microwave Tomography

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2nd International conference on Applied Information Technology and Innovation (ICAITI)

作者： Siburian, Sebastian Wijaya, Sastra Kusuma Prajitno, Prawito Univ Indonesia Dept Phys Depok Indonesia

ISBN: (纸本)9781728130163

In this research study, an investigation of the benefit of wavelet-based image fusion algorithm for enhancing the quality of the reconstructed images in a multi-frequency microwave tomography was conducted. The microwave tomography system which consists of a PocketVNA, a pair of Vivaldi antenna and a set of Arduino-based electromechanical system was used to acquire the scattering wave around an object being observed. The electromechanical was used to move the angular positions of the Vivaldi antenna pair in the scanning process in order to measure the reflection coefficient (S11), magnitude and phase of the microwaves interacted with observed object material. The Vivaldi antenna works in the range of 1.5 - 9.0 GHz, while the PocketVNA operates in range of 500 kHz - 4 GHz. Experiments were done to test the performance of the system with types of materials of different shapes and sizes. The reflection coefficient data (S11) resolved and reconstructed into an image via MATLAB based on Born approximation reconstruction algorithm. image reconstruction per single frequency is done sequentially from low frequency to high frequency, with a total of 6 different frequency values. A multi-frequency approach will be done by combining the element of stability from the effect of using low frequencies and high-resolution element from the effect of relatively higher frequency usage. The use of multi-frequency reduces nonlinearity problem and increases the stability to get an optimal image reconstruction. The used image fusion algorithm was also tested using the datasets from Fresnel Institute in order to verify its performance. The image yielded from the image fusion algorithm has a significant increasing image quality compared to the individual images from the reconstruction process resulted on single frequency usage without the image fusion process.

关键词： microwave tomography multi-frequency image fusion

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The 2 and 3 materials scene reconstructed from some line Mojette projections

The 2 and 3 materials scene reconstructed from some line Moj...

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International conference on image Processing Theory, Tools and Applications

作者： Guédon, Jeanpierre Liu, Chuanlin IRCCyN-IVC UMR CNRS 6597 Ecole Poly Technique University of Nantes La Chantrerie rue Christian Pauc 44306 Nantes France

ISBN: (纸本)9781424472482

Discrete tomography generally focus on binary image reconstruction from two projections. The Mojette transform allows for a more general framework with any kind of values and any number of projections. Here we use the Mojette transform to adress the problem of the 3 materials reconstruction. A new Mojette algorithm is derived and presented in the case of sparse data (reduce number of projections). This algorithm is also generalized for different other uses as for a binary scene reconstruction. ©2010 IEEE.

关键词： image reconstruction

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COMBINATION OF TERRESTRIAL LASERSCANNING, UAV And CLOSE-RANGE PHOTOGRAMMETRY FOR 3D reconstruction OF COMPLEX CHURCHES IN GEORGIA 2

COMBINATION OF TERRESTRIAL LASERSCANNING, UAV AND CLOSE-RANG...

引用

2nd International conference of Geomatics and Restoration (GEORES)

作者： Luhmann, T. Chizhova, M. Gorkovchuk, D. Hastedt, H. Chachava, N. Lekveishvili, N. Jade Univ Inst Appl Photogrammetry & Geoinformat Oldenburg Germany Univ Bamberg Digital Technol Heritage Conservat Bamberg Germany Kyiv Natl Univ Construct & Architecture Kiev Ukraine Georgian Tech Univ Fac Architecture Urban Planning & Design Tbilisi Georgia

In September 2018, photogrammetric images and terrestrial laser scans were carried out as part of a measurement campaign for the three-dimensional recording of several historic churches in Tbilisi (Georgia). The aim was the complete spatial reconstruction with a spatial resolution and accuracy of approx. 1 cm under partly difficult external conditions, which required the use of different measurement techniques. The local measurement data were collected by two laser scanning campaigns (Leica BLK360 and Faro Focus 3D X330), two UAV flights and two terrestrial image sets. The photogrammetric point clouds were calculated with the SfM programs AgiSoft PhotoScan and RealityCapture taking into account the control points from the Faro laser scan. The mean residual errors from the registrations or photogrammetric evaluations are 4-12mm, depending on the selected software. The best completeness and quality of the resulting 3D model was achieved by using laserscan data and images simultaneously.

关键词： Laserscanning Photogrammetry data fusion 3D reconstruction Cultural heritage

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CS Regularized SENSE pMRI reconstruction via Interferometric Modulation 2

CS Regularized SENSE pMRI Reconstruction via Interferometric...

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2nd IEEE International conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Islam, Sheikh Rafiul Maity, Santi P. Ray, Ajoy Kumar Neotia Inst Technol Management & Sci South 24 Parganas 743368 W Bengal India Indian Inst Engn Sci & Technol Howrah 711103 W Bengal India

ISBN: (纸本)9781509044429

Compressed Sensing (CS) or Compressive Sampling offers an improved data acquisition rate for Parallel Magnetic Resonance Imaging (pMRI) systems to achieve reduced scanning time. In pMRI, the optimization of image quality is done through appropriate selection/ placement of coils according to the anatomy of the object to be imaged. This paper proposes an efficient interferometric modulation scheme for radio frequency (RF) receiver coils of parallel MRI (pMRI) that produces magnetization field over the object. The modulated magnetization field is beneficial for improving estimation accuracy of sensitivity profiles which enhance reconstruction quality at high data acquisition rate. A CS regularized sensitivity encoding approach is used as reconstruction technique in which the required MR image is provided through an iterative optimization process from the under-sampled observed k-space data. Extensive simulation results show a significant reduction in artifacts and thereby consequent visual improvement in the reconstructed MR images are achieved even when a high rate of acceleration factor is used. Simulation results also demonstrate that the proposed method outperforms some state-of-the-art pMRI methods, both in terms of subjective and objective quality assessment for the reconstructed images.

关键词： Compressed sensing interferometer parallel imaging pMRI image reconstruction SENSE

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2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2017

2nd International Workshop on Simulation and Synthesis in Me...

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2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2017

ISBN: (纸本)9783319681269

The proceedings contain 11 papers. The special focus in this conference is on Simulation and Synthesis in Medical Imaging. The topics include: Adversarial image synthesis for unpaired multi-modal cardiac data;deep MR to CT synthesis using unpaired data;synthesizing CT from ultrashort echo-time MR images via convolutional neural networks;a supervoxel based random forest synthesis framework for bidirectional MR/CT synthesis;region-enhanced joint dictionary learning for cross-modality synthesis in diffusion tensor imaging;virtual PET images from CT data using deep convolutional networks;semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets;high order slice interpolation for medical images;a monte carlo framework for low dose CT reconstruction testing;multimodal simulations in live cell imaging;medical image processing and numerical simulation for digital hepatic parenchymal blood flow.

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