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

共 12035 条记录，以下是151-160 订阅

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Autism Spectrum Disorder Detection Using Convolutional Neural Network

Autism Spectrum Disorder Detection Using Convolutional Neura...

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2025 international conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Petricia, V. Angel Jacksy, M. Bhavadharani, R. St. Joseph's Institute of Technology Dept. of Computer Science and Engineering Chennai119 India

ISBN: (纸本)9798331521394

Autism Spectrum Disorder(ASD) is a complex neurodevelopmental condition marked by challenges in social interaction, communication, and repetitive behaviors. Early detection is critical to enable timely intervention and improve outcomes for individuals with ASD. This research proposes an innovative framework that integrates Convolutional Neural Networks (CNNs) with advanced data preprocessing techniques, facial imaging, and behavioral analysis to enhance the accuracy and efficiency of ASD diagnosis. Leveraging data augmentation and transfer learning, the model achieves robust performance with high sensitivity and specificity. The novelty of this approach lies in its ability to identify subtle facial and behavioral patterns indicative of ASD, using a multidisciplinary strategy that combines neuroimaging, machine learning, and real-time diagnostic feedback. By advancing beyond traditional methods, this comprehensive framework holds the potential to revolutionize early ASD detection, making it more accessible, scalable, and precise. © 2025 IEEE.

关键词： Convolutional neural networks

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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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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...

引用

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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3D Reconstruction of Colonic Polyps: A Methodology for Improved visualization and Analysis

3D Reconstruction of Colonic Polyps: A Methodology for Impro...

引用

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

作者： Ishtiaque Ahmed Pranto Saha Md. Saimim Islam Khan Hamim Irfan Ahmad Alvi Md. Zahid Hasan Md. Hasan Imam Bijoy Department of Computer Science and Engineering Health Informatics Research Laboratory (HIRL) Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Polyps are critical abnormalities that may indicate the initial phases of colon cancer, making accurate detection and analysis essential in medical diagnostics. Traditional 2D medical imaging techniques provide limited insight into the shape and structure of polyps, which is where 3D reconstruction can play a transformative role. By converting 2D polyp images into 3D models, healthcare professionals can gain enhanced visualization, enabling better diagnosis, treatment planning, and surgical precision. This study demonstrates a robust process for reconstructing polyp images in 3D using the Kvasir-Seg dataset, which includes 1,000 polyp images with expertly annotated ground truth masks. Our methodology includes the generation of depth maps using the Intel DPT-Large model, the extraction of point clouds with a customized approach, and the construction of precise mesh objects with Open3D. We extract geometric properties, including linearity, planarity, and curvature change, from the point clouds and meshes for a more in-depth analysis of the 3D objects. The unsupervised analysis of the resulting density plots indicates the presence of two distinct clusters within the feature distributions, highlighting the significance of 3D reconstruction for improved polyp characterization. Furthermore, the evaluation included 2D features such as edge density, contrast, dissimilarity, homogeneity, energy, correlation, entropy, number of lines, circularity, and Gabor energy. The analysis of p-values reveals that the 3D features related to these two clusters show notably lower p-values, thereby demonstrating their significance in the comprehensive analysis.

关键词： Point cloud compression visualization Solid modeling Three-dimensional displays Accuracy Shape Surgery Transforms Planning Image reconstruction

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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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Real-ESRGAN in Neural Rendering Frameworks for High-Fidelity Video Upscaling

Real-ESRGAN in Neural Rendering Frameworks for High-Fidelity...

引用

Advanced Computing Technologies (ICoACT), international conference on

作者： Sarthak Das Shivani Kamboj Department of Mathematics (of Aff.) Chandigarh University Chandigarh India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

As digital video content continues to grow, the need for enhanced visual quality has become increasingly important. However, utilizing the Real-ESRGAN video module can often lead to issues such as frame skipping and inconsistent enhancement results. This paper tackles these issues by introducing a novel approach that enhances individual frames of a video separately. By converting the original video into distinct frames and applying Real-ESRGAN for enhancement, we ensure that each frame is treated individually, thereby eliminating frame skips and achieving uniform enhancement across the video. After processing, the frames are reassembled into a cohesive video output. Our extensive evaluations reveal significant improvements in visual fidelity compared to conventional enhancement techniques. This research highlights the promise of integrating neural rendering methods in various fields, including film, gaming, and online streaming, and provides valuable contributions to the evolving landscape of video processing.

关键词： Measurement visualization Laplace equations Streaming media Media Rendering (computer graphics) Real-time systems Computational efficiency Image restoration Image reconstruction

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Advancing Healthcare with GPGPU and Energy Efficient NoC Architectures: A Comprehensive Survey

Advancing Healthcare with GPGPU and Energy Efficient NoC Arc...

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Ambient Intelligence in Health Care (ICAIHC), international conference on

作者： Panthagani Vijaya Babu Sonal Yadav Vijay Holimath Dept. of Computer Science & Engineering ViBram Silicon Hub National Institute of Technology Raipur India VividSparks IT Solutions Bangalore India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

General-purpose graphics Processing Units (GPGPU) have emerged as a transformative technology in healthcare and medical fields, harnessing their powerful parallel processing capabilities to handle complex computational challenges. Initially developed for graphics rendering, GPUs are now employed in high-performance tasks such as medical imaging, genomic analysis, drug discovery, and real-time patient monitoring, where vast data volumes and intensive computations are prevalent. GPGPU technology enhances processing efficiency, reduces latency, and supports faster, more accurate diagnostics and treatment planning. Complementing this advancement, Networks-On-Chip (NoC) designs, introduced in the early 2000s, have become a standard communication backbone for high-end CPUs and Systems-on-Chip (SoCs). Their low communication latency, high throughput, and energy efficiency make NoCs ideal for addressing the growing demands of GPU-based systems. However, achieving these performance objectives requires minimizing power dissipation, energy consumption, and costs. This research provides a comprehensive survey of NoC design models for multi-GPU systems, focusing on their role in energy-efficient and scalable architectures. It also highlights the impact of GPGPU in revolutionizing healthcare by meeting modern medical applications' computational and efficiency requirements.

关键词： Surveys Scalability Memory management graphics processing units Medical services Throughput Energy efficiency Real-time systems Computational efficiency Standards

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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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13th international Joint conference on computer Vision, imaging and computer graphics Theory and Applications, VISIGRAPP 2018

13th International Joint Conference on Computer Vision, Imag...

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13th international Joint conference on computer Vision, imaging and computer graphics Theory and Applications, VISIGRAPP 2018

ISBN: (纸本)9783030267551

The proceedings contain 18 papers. The special focus in this conference is on computer Vision, imaging and computer graphics Theory and Applications. The topics include: Flash, storm, and mistral: Hardware-friendly and high quality tone mapping;a simple and exact algorithm to solve linear problems with 1-based regularizers;spatial and spectral calibration of a multispectral-augmented endoscopic prototype;random forests based image colorization;a survey on databases of facial macro-expression and micro-expression;real-time head pose estimation by tracking and detection of keypoints and facial landmarks;contact-less, optical heart rate determination in the field ambient assisted living;effective facial expression recognition through multimodal imaging for traumatic brain injured patient’s rehabilitation;3D articulated model retrieval using depth image input;haptic and touchless user input methods for simple 3D interaction tasks: Interaction performance and user experience for people with and without impairments;recommendations from a study of a multimodal positive computing system for public speaking;utilisation of linguistic and paralinguistic features for academic presentation summarisation;An ROI visual-analytical approach for exploring uncertainty in reservoir models;a descriptive attribute-based framework for annotations in data visualization;tabularVis: An interactive relationship visualization tool supported by optimization and search algorithms;visual computing methods for assessing the well-being of older people.

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