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检索条件"任意字段=International Conference on Computer Graphics, Imaging and Visualization"

共 12023 条记录，以下是161-170 订阅

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FractureAI Revolutionizing Bone Fracture Detection with Deep Learning CNNs for Precision Medical imaging

FractureAI Revolutionizing Bone Fracture Detection with Deep...

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Automation and Computation (AUTOCOM), international conference on

作者： I. Sudha P.S. Ramesh P Durgadevi G. Sundari Senthilvadivu. S Sudha Narang Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani campus Chennai-26 Department of CSE C. Abdul Hakeem College of Engineering and Technology Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai TamilNadu India Maharaja Agrasen Institute of Technology Rohini Delhi

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Detection of bone fractures is part and parcel of clinical diagnosis, previously relied on radiological interpretation of X-ray or CT scans. However, manually reviewed system involves human intervention and it is vulnerable to reveal more false negatives or delayed at revealing certain fractures. To counter these challenges, this research proposes Fracture AI, a deep learning system that employs CNNs, specifically ResNet-50 for fracture identification and categorization. With the help of transfer learning and techniques such as Grad-CAM for fracture localization, the system can reach remarkable results with the accuracy of 96.2%, precision of a 97.9%, recall of a 96.9%, and the F1-score of a 97.4%. The results presented here show how Fracture Ai can enhance the diagnostic process enabling the identification of fractures in a shorter time, especially in emergency departments, has been clearly shown here and the AI solution is accurate.

关键词： Deep learning Training visualization Accuracy Computed tomography Transfer learning Bones X-ray imaging Medical diagnostic imaging Testing

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18th international Joint conference on computer Vision, imaging and computer graphics Theory and Applications, VISIGRAPP 2023

18th International Joint Conference on Computer Vision, Imag...

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18th international Joint conference on computer Vision, imaging and computer graphics Theory and Applications, VISIGRAPP 2023

ISBN: (纸本)9783031667428

The proceedings contain 17 papers. The special focus in this conference is on computer Vision, imaging and computer graphics Theory and Applications. The topics include: Epipolar Equation Weighting for Accurate Camera Motion from Two Consecutive Frames;Absolute-ROMP: Recovering Multi-person 3D Poses and Shapes with Absolute Scales from a Single RGB Image;semi-supervised Task Aware Image-to-Image Translation;deep Detection Dreams: Enhancing visualization Tools for Single Stage Object Detectors;approaches to Face Verification Through Attribute-Based Attention;towards Fast Detection and Classification of Moving Objects;ST-SACLF: Style Transfer Informed Self-attention Classifier for Bias-Aware Painting Classification;attention to Emotions: Body Emotion Recognition In-the-Wild Using Self-attention Transformer Network;linking Data Separation, Visual Separation, Classifier Performance Using Multidimensional Projections;using Cockpit Interactions for Implicit Eye-Tracking Calibration in a Flight Simulator;evaluation of Flexible Structured Light Calibration Using Circles;GPS-Enhanced RGB-D-IMU Calibration for Accurate Pose Estimation;application of Contrast Driven Color-Class Assignment to Four Categorical Data visualization Diagrams;measuring and Interpreting the Quality of 3D Projections of High-Dimensional Data;visualizing Military Operations: Extended Geospatial-Temporal Survey.

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Lab Color Model and Contrast Modification as a Tool for Improving the Quality of Medical Images

Lab Color Model and Contrast Modification as a Tool for Impr...

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Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Amer Abu-Jassar Mohammad Hamdan Kawther Hamash Sardor Boboyorov Vyacheslav Lyashenko Department of Computer Science College of Information Technology Amman Arab University Amman Jordan College of software engineering Amman Arab University Amman Jordan WellStar school of nursing Kennesaw state university Georgia USA Tashkent Medical Academy Termiz branch Termiz Uzbekistan Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper addresses the challenge of enhancing the quality of medical data interpretation, specifically using color microscopic medical images for effective visualization and further analysis. The method employed involves increasing contrast by adjusting the brightness intensity values within the Lab color model, which also considers the reference white point for horizontal light. To determine the parameters for the contrasting procedure, two strategies for selecting gamma correction of the input image are explored, based on the dynamics of the resulting image quality for various no-reference quality metrics. By analyzing these dynamics, the choice of correction conditions for the contrasting procedure can be justified, rather than relying on static metrics. The paper investigates the application of these proposed strategies on different types of images, presents a formal generalization of the strategies for selecting correction parameters, and offers practical recommendations for their use.

关键词： Measurement Image quality visualization Image color analysis Microscopy Computational modeling Data visualization Quality assessment Computational intelligence Biomedical imaging

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Augmented Reality for Enhanced Surgical Precision: A Framework for Real-Time visualization and Navigation

Augmented Reality for Enhanced Surgical Precision: A Framewo...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Jagbir Singh Jagadeesh Vedula Hariharan Ragothaman Shojonov Muzaffar Urazbaeva Yulduz Satendra Pratap Singh India Digital Transformation Lead Meghaz Inc. Houston Texas USA Head of the Information Security department of the Digital Education Technologies Center of Urgench State University Pedagogy and psychology department Mamun university Khiva Uzbekistan Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The advancement of Augmented reality (AR) technology has made it possible to use it in surgical precision and better the lives of patients. This paper outlines a real-time visualization and navigation framework where AR is integrated into surgical work. CT and MRI imaging data are used in the system to create accurate and rather detailed preoperative 3D anatomical models. These models are overlaid on the surgeon's vision, being the integral part of the system, providing the surgeon with dynamic constant assistance during procedures. To surmount the problem of misalignment of the virtual overlays with the patient's anatomy due to movements of the patient in the operating room, the framework uses complex tracking algorithms as well as machine learning techniques. It highlights planner pre-operative, intra-operative visualization and decision support mainly in complicated surgical cases. In the case of minimally invasive surgery, the framework was used to position the instruments correctly with improved accuracy, cutting short on time to operate, and countering complications. This research shows how AR can benefit today's surgery, as an origination of increased accuracy and a closing of the gap between conception and operations. It has also discussed some of the issues like scalability, cost, and the required training for the surgeon for the use of such system and has therefore provided a direction for the large-scale use of AR-assisted surgical systems.

关键词： Solid modeling Accuracy Three-dimensional displays Minimally invasive surgery Costs Navigation Surgery Machine learning Real-time systems Augmented reality

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19th Chinese conference on Image and graphics Technologies and Applications, IGTA 2024

19th Chinese Conference on Image and Graphics Technologies a...

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19th Chinese conference on Image and graphics Technologies and Applications, IGTA 2024

ISBN: (纸本)9789819799183

The proceedings contain 31 papers. The special focus in this conference is on Image and graphics Technologies and Applications. The topics include: Optimized Recognition and Depth Estimation for Fruit Picking Robots;BM3D-UGanNet: A Hybrid Deep Learning Network for Low-Light Thangka Image Enhancement;stagewise Positional Encoding for Implicit Neural Representation of Image;image Enhancement Based on a Diffusion Model Guided by No-Reference Image Quality Assessment;adaptive Pixel Pair Generation Strategy for Image Matting Methods Based on Pixel Pair Optimization;EdgCNN: Thangka Line Drawing Extraction Based on CNN;fire Detection Based on Flame Enhancement for Weak Fires;HDR-TDC: High Dynamic Range imaging with Transformer Deformable Convolution;enhancing Fine-Tuning Performance of Text-to-Image Diffusion Models for Few-Shot Image Generation Through Contrastive Learning;generalized Diffusion Models for Non-gaussian Noise;phase Error Correction Algorithm Based on Complementary Gray Code and Reverse Error Compensation;defect Detection Based on Normalized Attention Mechanism and Multi-level Feature Fusion;Study on Different Apple Ripeness Detection Based on Improved YOLOv5;learning Features by Minimizing the Interframe Differences;VTR-Former: Vision Token Rolling Transformer for Weakly Supervised Temporal Text Localization;CMME-YOLO: Composite Microscale Multi-stream Enhanced Model for PCB Defect Detection;multi-behavior Recommendation with Hypergraph Contrastive Learning;contrastive Learning-Based Dual Path Fusion Network for Group Activity Recognition;microclimate Regulation in Glass Greenhouses: Simulation of Wind Velocity and Temperature Fields and Design Model of the Greenhouse Fans;visualization of Spatial and Temporal Information of Railway Industrial Heritage in Modern China;Exploration of New Energy Electric Vehicle Development Based on SSA-SVR Model;long Sequences Generation for Motion Diffusion Models;Gaussian Replacement: Gaussians-Mesh Joint Rendering for Rea

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SkinNet: An EnsembleNet Technique to Detect Skin Cancer Using Pre-Trained Models

SkinNet: An EnsembleNet Technique to Detect Skin Cancer Usin...

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international conference on Electrical, computer and Communication Engineering (ECCE)

作者： Jasmin Jahan Puspo Muhammad Mahir Hasan Chowdhury Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh Data Science Technische Universität Dortmund Dortmund Germany

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Skin cancer is the most common cancer that causes death in both men and women. However, early detection can prevent the death rate. In this study, an ensemble-net technique to classify skin lesions as benign or malignant is proposed. Pre-trained convolutional neural networks (CNNs) architecture, InceptionV3, ResNet50, and DenseNet-121 are used as the base model after different experiments. All the experiments are conducted using the Nadam and Adam optimizers, with varying learning rates for each optimizer, and extensive pre-processing techniques. The dataset is collected from the ISIC 2018 dataset which contains images of two categories benign and malignant for training, validation, and testing sets. The proposed pipeline demonstrates high performance, with an accuracy of 98.59% on training, and 98% on testing compared to the other state-of-the-art methods for skin cancer disease prediction. The findings of this study are expected to be useful in cancer diagnosis research and potentially assist dermatologists in clinical decision-making.

关键词： Training visualization Accuracy Computational modeling Transfer learning Skin Medical diagnostic imaging Skin cancer Testing Residual neural networks

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BioTwinNet: Dual-Stream Multilevel Feature Fusion for Classification of SARS-CoV-2 and Influenza Virus Variants via Genomic Image Processing

BioTwinNet: Dual-Stream Multilevel Feature Fusion for Classi...

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

作者： Vatsal Shah Love Fadia Mohammad Hassanzadeh Majid Ahmadi Jonathan Wu Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The fast evolution and high transmissibility of emerging infectious illnesses like Influenza and SARS-CoV-2 pose serious public health problems. In order to effectively classify SARS-CoV-2 and Influenza variations, this study presents BioTwinNet, a unique dual-stream deep learning framework that integrates multilevel feature fusion with genomic image processing. Using methods like Wavelet Analysis, Discrete Cosine Transform, and Power Spectral Density, DNA sequences were converted into visual representations. BioTwinNet, which makes use of the depth capabilities of ResNet architectures and the hierarchical feature extraction of Convolutional Neural Networks, was used to evaluate these genomic images. By using Discrete Cosine Transform-Power Spectral Density characteristics, this method achieves an impressive classification accuracy of 97.42%, overcoming the difficulties caused by differences in sequence length.

关键词： Deep learning visualization Genomics Influenza Signal processing Wavelet analysis Coronaviruses Convolutional neural networks Bioinformatics Biomedical imaging

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WaveFilter: Advanced imaging for Marine Microplastic Monitoring

WaveFilter: Advanced Imaging for Marine Microplastic Monitor...

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

作者： Deepali Jadhav Amruta Amune Bhairavi Shirsath Suraj Chavan Prathmesh Sonawane Tanishk Shrivastava Ishan Shivankar Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

YOLOv5 has proven to be an efficient deep learning model for the detection of microplastics in aquatic environments. It is compact, with a total number of 214 layers and 7,022,326 parameters, while its model size is approximately 14.4 MB. Extensive performance analysis was done that states that the model has a precision of 79.8% and a recall of 67.1%, hence proving efficiency in the detection of instances of microplastics. The performance metrics are that the model offers a mAP of 72.1% at IoU=0.5 and averaged mAP of 34.1% averaged across different IoU values. These also validate the strength of YOLOv5 to detect microplastic with complex backgrounds which could be applied to real-time, automated, computer vision-based frameworks for the detection of marine plastic pollution. These promising results from the study have opened further vistas for development in environment monitoring and conservation.

关键词： YOLO Deep learning Analytical models visualization Accuracy Computational modeling Real-time systems Robustness Monitoring Plastic waste

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Uav Detect, Track and Follow (DTF) of Non-Stationary Targets in Aerial Thermal Videos

Uav Detect, Track and Follow (DTF) of Non-Stationary Targets...

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international conference on computer and Automation Engineering, ICCAE

作者： Shivpal Singh K Sathya Babu Vasit Sagan Chandrakanth Vipparla K Palaniappan Hadi AliAkbarpour Computer Science Department IIIT - Design and Manufacturing Kurnool India Department of Earth Environmental and Geospatial Sciences Taylor Geospatial Institute St. Louis USA Electrical and Computer Science Department University of Missouri Columbia USA Department of Computer Science Saint Louis University St. Louis USA

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Evolution of portable graphics Processing Units (GPUs) have rejuvenated the research on real-time target tracking in aerial systems. These devices are highly optimized in terms of volume, size, power, weight and are effectively employed in UAV based applications. Tracking a target can be broadly divided into two sub-categories: (a) Surveillance and Tracking of the target from a distance and (b) Track and Follow towards the target(e.g. medical and e-commerce deliveries), where the UAV has to intercept the target as closely as possible. Surveillance applications are extensively studied and successfully implemented in real-time. However, present day target follow applications rely completely on the GPS data for intercepting the target. In this work, we propose a novel convolutional neural network (CNN) based target follow in GPS denied environments. The proposed application presents novel challenges in real-time with changes in aspect ratio, target orientation, size of the target in the Field of View (FOV) and track loss. This is further exacerbated in case of thermal systems due to limited information and low thermal contrast ratios between foreground and background. In this work, we address the problem of target follow specifically in thermal videos. We deployed the proposed solution on a portable NVIDIA GPU integrated on a custom developed UAV. The algorithm proposed is successfully tested and validated and the results obtained demonstrate the efficiency of the algorithm for aerial thermal datasets.

关键词： Target tracking Surveillance graphics processing units Infrared imaging Data collection Autonomous aerial vehicles Cameras Real-time systems Convolutional neural networks Global Positioning System

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Progressing Alzheimer's Diagnosis with Ensemble CNN Model

Progressing Alzheimer's Diagnosis with Ensemble CNN Model

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Disruptive Technologies (ICDT), international conference on

作者： Vivek Yadav Vaibhav Rohit Sushil Kumar Umang Rastogi CSE Department KIET Group of Institutions Ghaziabad India CSE Department Data Science & Deep Learning (DSDL) Lab KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This paper proposes an Alzheimer's disease prediction model using CNN, implemented on a system with AMD Radeon Vega 8 graphics to improve computational traceability. The research is encouraging because the implicates of the model touched the 97% accuracy of the disease prediction by MRI images of patients. The integration of the Vega 8 GPU allows the system to process large datasets and perform computations at a significantly faster rate, thereby reducing training time. The MRI dataset used in this study is highly imbalanced, with four classes: Non-Demented or Non-D, Very Mild Dementia or V.M.D, Mild Dementia or M.D, Moderate Demented or M.D.A. CNN was chosen for its capability in highlight extraction and learning from MRI pictures, without requiring human interference. The show was run with parallel and multiclass datasets, and IT got 97% exactness. The systematic analysis of the test shows that the system can diagnose Alzheimer's right at the initial stage of the disease development. This approach shows how CNNs with the aid of AMD Radeon Vega 8 graphics perform well in automating Alzheimer's diagnosis while breaking the barriers associated with manual feature extraction and orthodox dependence on experts.

关键词： Training Deep learning Accuracy Magnetic resonance imaging Computational modeling Transfer learning graphics processing units computer architecture Predictive models Alzheimer's disease

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