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检索条件"任意字段=3rd International Conference on 3-D Digital Imaging and Modeling"

共 1766 条记录，以下是11-20 订阅

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3rd international conference on Geometric modeling and imaging: Modern Techniques and Applications, GMAI

3rd International Conference on Geometric Modeling and Imagi...

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3rd international conference on Geometric modeling and imaging: Modern Techniques and Applications, GMAI

ISBN: (纸本)9780769532707

The proceedings contain 21 papers. The topics discussed include: three point stationary and non-stationary subdivision schemes;designing planar objects with an interactive spline;simulating a deformable object using a surface mass spring system;smooth surface reconstruction using doo-Sabin subdivision surfaces;a vector representation for polyhedra;shape analysis of left ventricle using invariant 3-d spherical harmonics shape descriptors;experimental evaluation of graphics algorithms;creative shape interpretation in paper scribbles;contour rectification and analysis using circular augmented rotational trajectory algorithm;a new contour reconstruction approach from dexel data in virtual sculpting;image restoration by non-standard diffusion;sub-tensor decomposition for expression variant 3d faces recognition;and a fingerprint local-matching algorithm using unit-circle parameterization.

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2d Sheet Temperature Estimation for Multi-Stage Press Hardening in a Progressive die 11

2D Sheet Temperature Estimation for Multi-Stage Press Harden...

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11th Vienna international conference on Mathematical Modelling, MATHMOd 2025

作者： Baumann, Henry Nazarenus, Jakob Martschin, Juri Tekkaya, Erman Meurer, Thomas Digital Process Engineering Group MVM Karlsruhe Institute of Technology Hertzstr. 16 Karlsruhe76187 Germany Multimedia Information Processing Group Kiel University Hermann-Rodewald Straße 3 Kiel24118 Germany Institute of Forming Technology and Lightweight Components TU Dortmund University Baroper Str. 303 Dortmund44227 Germany

during a multi-stage press hardening process, where a metal sheet undergoes rapid austenitization, tempering, and forming, the spatial-Temporal temperature development is a driving factor and its control enables it to reach desired product properties. A two-dimensional model extension is proposed, which enables a precise and computationally efficient temperature modeling along the whole metal sheet. Thereupon, a Kalman filter with a time-varying stage dependent output matrix is used to estimate the spatial-Temporal temperature development of the sheet based on measurements from a thermal imaging camera. Both the proposed modeling approach and the estimation scheme are validated experimentally. Copyright © 2025 The Authors.

关键词： Kalman filters

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detection Of Counterfeit Images Through deep Learning based Yolov3 Model

Detection Of Counterfeit Images Through Deep Learning based ...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Jobez J. C Golden Nancy R Syed Rabiya Mohamed Ali Muthukumaran N Raghavan Sheeja Jasmine Gnana Malar A Department of AIML Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. Engineering College Gummidipoondi Tamil Nadu India Department of ECE PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Image forgery is the procedure of analysing and detecting any fraudulent or unauthorised alterations made to digital images. The detection in images is difficult for achieving a high accuracy rate and the process is time-consuming. Researchers and forensic experts are developing tools for detecting fake images. The identification of computer-generated picture forgeries is a notable area of study, aiming to provide a comprehensive review of detection tools and techniques. In this work, a novel deep-learning model is proposed for detecting forgery in images. The images are pre-processed using an adaptive Gaussian bilateral (AGB) filter to enhance the quality of the images. The enhanced photos are fed into the Mobile Net for extracting the relevant features in the pictures. The modified Yolov3 is utilized for object detection in images and detecting the real and forgery images. According to the result, the proposed model attains 99.55% for forgery detection in images. The modified Yolov3 yields 99.55% accuracy as opposed to Mask RCNN, RCNN, and SSd's 0.68%, 0.65%, and 1.16% accuracy. CNN, ResNetV2 and dCNN are all outperformed the proposed model in terms of overall accuracy by 0.11%, 1.10%, and 5.55%, respectively.

关键词： YOLO deep learning Adaptation models Accuracy Reviews Computational modeling Forensics digital images Feature extraction Forgery

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Big data for Enhancing Cybersecurity in Enterprise Environments Proactive Threat detection and Prevention 3

Big Data for Enhancing Cybersecurity in Enterprise Environme...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Chityala, Upender Reddy Shnain, Ammar H. Govindaraj, Manoj Johri, Prashant Kuppuraj, T. devi, N.Lavanya Equinox It Solutions Llc United States College of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Chennai India School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Psna College of Engineering and Technology Department of Ece India

ISBN: (纸本)9798331542375

This research explores the integration of big data technologies and machine learning to enhance cybersecurity in enterprise environments focusing on proactive threat detection and prevention. Traditional security systems often struggle to keep up with the growing complexity and volume of cyber threats, necessitating a more advanced, data-driven approach. The proposed methodology leverages real-time data processing, predictive modeling, and anomaly detection to improve threat detection accuracy, reduce response times, and enhance system scalability. By collecting and analyzing data from various network sources, including logs from security devices and endpoint protection software, the system detects potential threats more efficiently than conventional methods. Machine learning algorithms such as supervised and unsupervised learning, along with big data platforms like Apache Spark, enable real-time analysis of large volumes of data, ensuring timely identification of both known and unknown cyber threats. The results demonstrate significant improvements in detection accuracy, system scalability, and incident resolution time, with the proposed methodology outperforming traditional security systems in key performance metrics. This research highlights the potential of big data-driven cybersecurity solutions in addressing modern challenges and offers a framework for organizations to implement more effective, scalable, and proactive security measures to safeguard their digital assets. © 2025 IEEE.

关键词： Cyber attacks

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A Critical Analysis on 3d Craniofacial Reconstruction Using GANs

A Critical Analysis on 3D Craniofacial Reconstruction Using ...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Kriti Gupta Meenu Gupta Rakesh Kumar Neha Gupta Venkata Suresh Babu Chilluri Department of CSE Chandigarh University Mohali India School of Computer Science and Engineering Geeta University Panipat Intuit Inc. MountainView CA USA

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

3d Face and Head Reconstruction has gained a significant attention in various fields like biometrics, entertainment, medical imaging, and etc. Traditional reconstruction methods (which take information from number of 2d photos and geometrical shapes of the item) was less accurate in defining features of an object. due to technological enhancement Generative Adversarial Networks (GANs) achieved a remarkable performance in finding the 3d face and head reconstruction features. This paper attempts to traverse the complexities of dataset selection and various hyperparameters of 3d craniofacial reconstruction in order to address the complex questions related to 3d face and head reconstruction using GANs and other approaches. This analysis draws on GANs as a general and proposed framework to provide insight into the fast-evolving field of 3d face and head reconstruction. To analyse the facts, 760 articles have been extracted based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Guidelines. After a rigorous analysis, 38 articles have been finalized for final consideration. The findings of this study concludes that GANs can be used to recreate faces and heads in 3d using dense mesh structures that are highly textured and have distinct edges and vertices.

关键词： Solid modeling Three-dimensional displays Shape Entertainment industry Reconstruction algorithms Generative adversarial networks Image reconstruction Faces Systematic literature review Guidelines

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A Novel 3d-CNN with dAG Framework for Enhanced Alzheimer's disease diagnosis Using Structural MRI

A Novel 3D-CNN with DAG Framework for Enhanced Alzheimer's D...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： d Prasad K Jayanthi Pradeep Tilakan Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India Department of Psychiatry Pondicherry Institute of Medical Sciences Puducherry India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Alzheimer's is the most common and neurodegenerative kind of dementia impacting the elderly. It is noteworthy that a limited number of older individuals with Alzheimer's disease are timely and effectively diagnosed in the early stages. This is an essential precondition, since the illness is more controllable and therapy is more successful when diagnosed at the beginning or early mild cognitive impairment stage. In recent years, several researchers have focused on enhancing prediction and diagnostic accuracy using an AI-driven image categorization methodology. This research proposes a novel 3d-CNN with directed Acyclic Graph architecture aimed at precise and efficient diagnosis of Alzheimer's disease in its early stages, utilizing segmented left and right hippocampus regions, grey matter, cerebrospinal fluid, and white matter extracted from structural MRI images of the brain. The structural MRI images undergo pre-processing using 3d Slicer and SPM12. This study utilizes the AdNI, NACC, and KAGGLE datasets. The findings are persuasive, since the suggested 3d-CNN with directed Acyclic Graph demonstrates superior performance achieving an accuracy of 96.86% relative to other current studies.

关键词： directed acyclic graph Accuracy Translation Magnetic resonance imaging Computational modeling Grey matter Alzheimer's disease White matter Older adults Hippocampus

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Proceedings of the 1997 1st international conference on Recent Advances in 3-d digital imaging and modeling

Proceedings of the 1997 1st International Conference on Rece...

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Proceedings of the 1997 1st international conference on Recent Advances in 3-d digital imaging and modeling

The proceedings contains 45 papers from the international conference on Recent Advances in 3-d digital imaging and modeling. Topics discussed include: range image sensors;laser rangefinders;view planning;view registration;geometric processing;object modeling;human shape modeling;three dimensional facial caricaturing;three dimensional models database management;portable three-dimensional digitizers;and automatic computer aided design modeling.

关键词： imaging systems

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Smart Glove for ENT Treatment

Smart Glove for ENT Treatment

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Surya Narayanan R d Sharmitha Kavinraj A S Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Electrical and electronic applications in medicine and health have greatly improved as technology has advanced. This includes anything from digital meters for measuring health factors to cutting-edge equipment that performs precise procedures on the body. This paved the way for a new and significant use of electronics known as "smart medical applications." This paper focuses on improving endoscopy conducted by ENT specialists to evaluate the insides of the ears, nose, and throat through image processing and convenient design, which will aid doctors by giving a more complete inspection of the human body. In the proposed method, a Raspberry Pi microcontroller is used, which is interfaced with a camera module and acquires images or footage of the area to be inspected before sending it to the controller in real-time. The acquired image or footage is then projected on an LCd screen so that the expanded image may be seen with greater clarity. The findings from simulations were gathered and verified using the Proteus simulation tool. The concept is feasible for implementation and has a vast scope for assisting doctors more efficiently.

关键词： Microcontrollers Endoscopes Prototypes Medical services Inspection Cameras Liquid crystal displays Hardware Biomedical imaging Testing

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3rd international conference on 3-d imaging and modeling (3dim 2001), Quebec City, Canada

3rd International Conference on 3-D Imaging and Modeling (3d...

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2001年

作者： IEEE Computer Society Staff

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TEPEN international Workshop on Fault diagnostics and Prognostics, TEPEN-IWFdP 2024

TEPEN International Workshop on Fault Diagnostics and Progno...

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TEPEN international Workshop on Fault diagnostics and Prognostics, TEPEN-IWFdP 2024

ISBN: (纸本)9783031734069

The proceedings contain 108 papers. The special focus in this conference is on Fault diagnostics and Prognostics. The topics include: Field Estimation of dynamic Coefficients of Journal Bearing Under Transient Operations;multiple Source domain Transfer Fault diagnosis Method in Rolling Bearing Under Variable Working Conditions;Short-Term Wind Power Prediction Based on OLHS-dBO-BP Neural Network;self-powered Multifunctional Artificial Skin Inspired by Human Skin;mechanical Fault diagnosis of High Voltage Circuit Breaker Based on Autoencoder and Metric Learning;research on Self-powered Sensors Based on Piezoelectric Energy Capture;Sensorless Control of PMSM Based on Sliding Mode Observer;experimental Study on Fault diagnosis of Wind Turbine Blades Based on Acoustics;Research on Visual Algorithm for Fire detection of Firefighting UAVs Based on Infrared imaging;Reliability modeling and Analysis of Elevator Brake Based on KdF Model Optimization;comparison of the Impact of Symbol Aggregation Approximation Encoding and Multiscale Encoding on Lempel-Ziv Complexity Results;marker-Assisted 3d Vibration Visual Measurement with a Single Camera;electrochemical Impedance Spectroscopy Characterization of Sodium-Ion Batteries with different Operating States;importance Analysis Based on Evidence Theory and Bayesian Network Reasoning;a New Method for Calculating Meshing Stiffness of Spur Gears Considering Misalignment with Multiple degrees of Freedom;Research on CNN-Attention Regression Prediction Method Based on GOA Optimization;SOC Estimation of Lithium Battery Based on Fractional Order AUKF Algorithm;digital Twin-Based Service System Platform;grid Maintenance Optimization Based on Particle Swarm Optimization;Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method;use Anchor-Free Based Object detectors to detect Surface defects.

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