The proceedings contain 39 papers. The special focus in this conference is on Applied Informatics. The topics include: Automatic Identification of Forest Areas in the "Carolina" Park Using Res...
ISBN:
(纸本)9783031751462
The proceedings contain 39 papers. The special focus in this conference is on Applied Informatics. The topics include: Automatic Identification of Forest Areas in the "Carolina" Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study;deep Convolutional Neural Network for Autonomic Function Estimation in Intensive Care Patients;deep Learning Techniques for Oral Cancer Detection: Enhancing Clinical Diagnosis by ResNet and DenseNet Performance;machine Learning Operations Applied to Development and Model Provisioning;MultiMF: A Deep Multimodal Academic Resources Recommendation System;netScribed: A Deep Learning Approach for Machine-Based Melody Transcription of Audio Files;Safeguarding Patient data: Advanced Security in Wireless Body Area Networks and AI-Driven Healthcare Systems;YOLO Convolutional Neural Network for Building Damage Detection in Hydrometeorological Disasters Using Satellite imagery;extraction and Selection of Multi-omic Features for the Breast Cancer Survival Prediction;migration from On-Premises to Cloud: Challenges and Opportunities;improvement of Acute and Chronic Nutritional Status by Supplying a Metabolic Biostimulator to Children in High Risk of Malnutrition. Introducing a Technological Platform to Enable Automatic Analyses and Reporting;relationship between Demographic, Geographical and Access to Health Services Factors;RGB imagereconstruction for Precision Agriculture: A Systematic Literature Review;Developing AI-Driven Cross-Platform Geolocation for Enhanced Strategic Decision-Making;fromdata to Decisions: Performance Evaluation of Retail Recommender Systems;Optimizing Fraud Detection in Traffic Accident Insurance Claims Through AI Models: Strategies and Challenges;Code Legends: RPG Game as a Support Tool for Programming in High School;development of Mental Agility Mini-games in a Video Game for Early Detection of Mild Cognitive Impairment: An Innovative Approach in Mental Health.
The proceedings contain 39 papers. The special focus in this conference is on Applied Informatics. The topics include: Automatic Identification of Forest Areas in the "Carolina" Park Using Res...
ISBN:
(纸本)9783031751431
The proceedings contain 39 papers. The special focus in this conference is on Applied Informatics. The topics include: Automatic Identification of Forest Areas in the "Carolina" Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study;deep Convolutional Neural Network for Autonomic Function Estimation in Intensive Care Patients;deep Learning Techniques for Oral Cancer Detection: Enhancing Clinical Diagnosis by ResNet and DenseNet Performance;machine Learning Operations Applied to Development and Model Provisioning;MultiMF: A Deep Multimodal Academic Resources Recommendation System;netScribed: A Deep Learning Approach for Machine-Based Melody Transcription of Audio Files;Safeguarding Patient data: Advanced Security in Wireless Body Area Networks and AI-Driven Healthcare Systems;YOLO Convolutional Neural Network for Building Damage Detection in Hydrometeorological Disasters Using Satellite imagery;extraction and Selection of Multi-omic Features for the Breast Cancer Survival Prediction;migration from On-Premises to Cloud: Challenges and Opportunities;improvement of Acute and Chronic Nutritional Status by Supplying a Metabolic Biostimulator to Children in High Risk of Malnutrition. Introducing a Technological Platform to Enable Automatic Analyses and Reporting;relationship between Demographic, Geographical and Access to Health Services Factors;RGB imagereconstruction for Precision Agriculture: A Systematic Literature Review;Developing AI-Driven Cross-Platform Geolocation for Enhanced Strategic Decision-Making;fromdata to Decisions: Performance Evaluation of Retail Recommender Systems;Optimizing Fraud Detection in Traffic Accident Insurance Claims Through AI Models: Strategies and Challenges;Code Legends: RPG Game as a Support Tool for Programming in High School;development of Mental Agility Mini-games in a Video Game for Early Detection of Mild Cognitive Impairment: An Innovative Approach in Mental Health.
The authors are interested in the reconstruction of 3-D objects from various sources of incompletedata as well as apriori knowledge about the object under reconstruction. As data sources use is made of conventional X...
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The authors are interested in the reconstruction of 3-D objects from various sources of incompletedata as well as apriori knowledge about the object under reconstruction. As data sources use is made of conventional Xray projections as well as CAT scans perpendicular to the Xray planes. These data sources were chosen because they are complementary to each other, in the sense that information not found in one set can be found in the other.
In industrial X-ray computerized tomography (CT), the objects to be inspected are usually very attenuating to X-rays, and their shape may not permit complete scannings at all view angles; incomplete-data imaging situa...
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In industrial X-ray computerized tomography (CT), the objects to be inspected are usually very attenuating to X-rays, and their shape may not permit complete scannings at all view angles; incomplete-data imaging situations usually result. In earlier reports, it was concluded that imagereconstructionfromincompletedata can be achieved effectively through an iterative transform algorithm, which utilizes the a priori information on the object to compensate for the missing data. The image is repeatedly transformed to the object space, where it is corrected by the a priori information on the object, and back to the projection space where it is corrected by the measured projections. The results of validating the iterative transform algorithm on experimental datafrom a cross section of a high-pressure turbine blade made of Ni-based superalloy are reported. from the data set, two kinds of incompletedata situations are simulated: incomplete projection and limited-angle scanning. The results indicate that substantial improvements, both visually and in wall thickness measurements, were brought about in all cases through the use of the iterative transform algorithm.
An estimation approach to three-dimensional reconstructionfrom parallel ray projections, with incomplete and very noisy data, is described. Using a stochastic dynamic model for an object of interest in a probed domai...
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An estimation approach to three-dimensional reconstructionfrom parallel ray projections, with incomplete and very noisy data, is described. Using a stochastic dynamic model for an object of interest in a probed domain of known background density, the reconstruction problem is reformulated as a n onlinear state estimation problem. An approximate minimum mean square error globally optimal algorithm for the solution of this problem is presented. The algorithm, which is recursive in a hybrid frequency-space domain, operates directly on the Fourier transformed projection data, eliminating altogether the attempt to invert the projection integral equation. The simulation example considered in this paper demonstrates that good object estimates may be obtained with as few as five views in a limited sector of 90° and at a signal-to-noise ratio as low as 0 dB.
X-ray differential phase-contrast computed tomography (DPC-CT) is a powerful physical and biochemical analysis tool. In practical applications, there are often challenges for DPC-CT due to insufficient data caused by ...
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X-ray differential phase-contrast computed tomography (DPC-CT) is a powerful physical and biochemical analysis tool. In practical applications, there are often challenges for DPC-CT due to insufficient data caused by few-view, bad or missing detector channels, or limited scanning angular range. They occur quite frequently because of experimental constraints from imaging hardware, scanning geometry, and the exposure dose delivered to living specimens. In this work, we analyze the influence of incompletedata on DPC-CT imagereconstruction. Then, a reconstruction method is developed and investigated for incompletedata DPC-CT. It is based on an algebraic iteration reconstruction technique, which minimizes the image total variation and permits accurate tomographic imaging with less data. This work comprises a numerical study of the method and its experimental verification using a dataset measured at the W2 beamline of the storage ring DORIS III equipped with a Talbot-Lau interferometer. The numerical and experimental results demonstrate that the presented method can handle incompletedata. It will be of interest for a wide range of DPC-CT applications in medicine, biology, and nondestructive testing.
Nowadays, pervasive vision through synthetic aperture radar (SAR) imaging sensors is a crucial part of air-borne and space-borne platforms to reach the concept of internet of multimedia things over satellites and visu...
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Nowadays, pervasive vision through synthetic aperture radar (SAR) imaging sensors is a crucial part of air-borne and space-borne platforms to reach the concept of internet of multimedia things over satellites and visual flying ad-hoc networks. SAR sensors always need high-performance datareconstruction techniques to provide quality of experience for end-users of the distributed surveillance nodes and ensuring reliable decision-making in the autonomous vehicles. Using prior information in SAR imagereconstruction, improves the quality of reconstructed image. Many sparse regularization methods use pre information terms in which the image is sparsely presented based on a predefined dictionary. However, if the desired features of the real image have not a sparse representation based on the predefined dictionary, imagereconstruction with enhanced interested features will be failed. Dictionary learning to better adapt with the underlying scene can lead to better imagereconstruction. On the other hand, using the idea of dictionary learning in the imagereconstruction problem, and considering that the data used to train the dictionary may be incomplete and noisy, will create limitations for this method. In this paper, a new idea based on the use of an overcomplete dictionary consisting of a learned dictionary and a predefined dictionary (pre-learned dictionary) for SAR imagereconstruction problem is presented. Developing the necessary mathematical relations and providing the framework for SAR imagereconstruction based on the use of the overcomplete pre-learned dictionary, to simultaneously enhance the features considered in advance and the features associated with the image, fromincomplete and noisy SAR raw data is presented in this article. We also present an iterative algorithm for solving the corresponding optimization problem. Simulation results based on the real data of TerraSAR-X and the Lynx airborne system show the effectiveness of the proposed method.
imagereconstructionfromincomplete measurements is one basic task in imaging. While supervised deep learning has emerged as a powerful tool for imagereconstruction in recent years, its applicability is limited by i...
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imagereconstructionfromincomplete measurements is one basic task in imaging. While supervised deep learning has emerged as a powerful tool for imagereconstruction in recent years, its applicability is limited by its prerequisite on a large number of latent images for model training. To extend the application of deep learning to the imaging tasks where acquisition of latent images is challenging, this article proposes an unsupervised deep learning method that trains a deep model for imagereconstruction with the access limited to measurement data. We develop a Siamese network whose twin sub-networks perform reconstruction cooperatively on a pair of complementary spaces: the null space of the measurement matrix and the range space of its pseudo inverse. The Siamese network is trained by a self-supervised loss with three terms: a data consistency loss over available measurements in the range space, a data consistency loss between intermediate results in the null space, and a mutual consistency loss on the predictions of the twin sub-networks in the full space. The proposed method is applied to four imaging tasks from different applications, and extensive experiments have shown its advantages over existing unsupervised solutions.
In this letter, we compare the performances of sparse recovery algorithms (SRAs) for the reconstruction of a 2-D inverse synthetic aperture radar (ISAR) imagefromincomplete radar-cross-section (RCS) data. The three ...
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In this letter, we compare the performances of sparse recovery algorithms (SRAs) for the reconstruction of a 2-D inverse synthetic aperture radar (ISAR) imagefromincomplete radar-cross-section (RCS) data. The three methods considered for the SRA include the basis pursuit (BP), the BP denoising, and the orthogonal matching pursuit methods. The performances of the methods in terms of the reconstruction accuracy of the ISAR image are compared using the incomplete RCS data. In addition, traditional interpolation methods such as nearest-neighbor interpolation, linear interpolation, and spline interpolation are applied to the incomplete RCS data to reconstruct ISAR images, and their performances are compared to that of the SRAs.
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