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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111-120 订阅

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Design and Evaluation of a Hybrid Adder Employing the Power Gating Method

Design and Evaluation of a Hybrid Adder Employing the Power ...

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Visual Analytics and Data visualization (ICVADV), international conference on

作者： Sowmiya K S.Praveen Kumar T. Aravind S. Ramya Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The multiplexers (mux) select the final sum and carry, rendering the CSLA an inefficient area adder. Two rows of ripple carry adders are present. While the suggested AFPL logic can reduce power dissipation in the CBL based SQRT CSLA, the CBL based SQRT CSLA is preferred to address the problems of area efficiency and latency. The major objective is to use AFPL logic to develop a CBL-based SQRT CSLA-hybrid adder that will increase speed and reduce power dissipation.

关键词： Multiplexing Visual analytics Data visualization Logic gates Rendering (computer graphics) CMOS technology Hybrid power systems Power dissipation Logic Adders

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Integrating Deep Learning Frameworks for Automated Medical Image Diagnosis 8th

Integrating Deep Learning Frameworks for Automated Medical I...

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8th IFIP TC 12 international conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Gayatri, K. Kumar, K. M. Anand Padmavathi, B. Shankar Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai600089 India

ISBN: (纸本)9783031736162

In today’s healthcare system, medical image analysis is essential for diagnosis, treatment planning, and condition monitoring. In this study, the four well-known tools in the field are 3D Slicer, MONAI, SAMM, and YOLOv8 are examined and contrasted. The study’s scope includes its ability to process a variety of 2D and 4D medical imaging datasets, which contain images, segmentation, annotations, and transformations. The process entails a comprehensive prehensive examination of every tool’s feature, with particular emphasis on their capacity to read and write DICOM images, accommodate multiple file formats, offer interactive 3D Slicer. Key findings show that 3D Slicer performs exceptionally well when using deep learning techniques for segmentation and interactive visualization. While SAM Model exhibits adaptability in managing diverse segmentation prompts, MONAI provides a comprehensive end-to-end medical data processing solution. YOLOv8 shows potential for effective object detection. The study’s implications include the possibility of using these instruments in clinical practices and medical research contexts. The research results provide insight into the current discussion about how to strategically integrate state-of-the-art methods to improve overall healthcare outcomes, diagnostic accuracy, and treatment planning efficiency as medical image analysis continues to advance. © IFIP international Federation for Information Processing 2025.

关键词： Clinical research

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Dual-Structural Representation Learning with Attention-Based Graph Fusion for Hierarchical MCI-AD Diagnosis 17th

Dual-Structural Representation Learning with Attention-Based...

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17th international conference on Brain Informatics, BI 2024

作者： Cao, Yu Kuai, Hongzhi Wang, Bixin Fan, Fengmei Peng, Guanqiao Zhong, Ning Faculty of Information Technology Beijing University of Technology Beijing China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China Faculty of Engineering Maebashi Institute of Technology Gunma Maebashi Japan Peking University Huilongguan Clinical Medical School Beijing Huilongguan Hospital Beijing China College of Liberal Arts and Sciences University of Illinois Urbana-Champaign Illinois United States

ISBN: (纸本)9789819632930

The integration of AI, big data, and magnetic resonance imaging (MRI) has significantly advanced healthcare, particularly in the early diagnosis and treatment of neurodegenerative diseases. Alzheimer’s Disease (AD), which predominantly affects the elderly, and its precursor, Mild Cognitive Impairment (MCI), present considerable challenges in early detection due to the complex structural changes in the brain. Traditional diagnostic methods often struggle to capture these intricate changes. To address this, the systematic Brain Informatics methodology is reconsidered to realize evidence combination based on dual-structural representation and fusion computing via attention-based graph. In particular, the DS-AGF (Dual-Structural Representation Learning with Attention-Based Graph Fusion) model was proposed for hierarchical MCI-AD diagnosis. This model employs a dense convolution unit and a node-optimized convolution unit to learn multi-level, fine-grained brain representations. Additionally, an attention-based graph representation fusion unit is introduced, enabling the integration of these representations and allowing the model to capture both local and global relationships between brain regions. This approach enhances the network feature learning ability on critical features, enabling multi-view visualization of key brain regions and their connectivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Magnetic resonance imaging

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Attention-Based Multi-Scale Fusion for Brain Tumor Classification with Explainable AI

Attention-Based Multi-Scale Fusion for Brain Tumor Classific...

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

作者： Md. Asraful Sharker Nirob Prayma Bishshash Tania Khatun Shayla Sharmin Md Zahid Hasan Mohammad Shamsul Arefin Department of Computer Science & Engineering Daffodil International University Dhaka Bangladesh Division of Computer Science and Engineering Louisiana State University Baton Rouge Louisiana USA Department of Computer Science & Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Accurate classification of brain tumors from MRI scans is crucial for effective diagnosis and treatment planning in neuro-oncology. This study presents a comprehensive framework leveraging advanced deep-learning techniques and visualization methods to achieve precise tumor classification. Our methodology involves merging two diverse datasets, performing data augmentation, and standardizing image dimensions to facilitate robust model training. We propose an attention-based multiscale fusion model, which integrates spatial attention and multi-scale fusion layers, achieving an accuracy of 99.17%, surpassing other models such as DenseNet201 (87.21%), InceptionV3 (82.30%), and MobileNet V3 (92.01%). Advanced visualization techniques, including Grad-CAM, enhance interpretability and confidence in diagnostic assessments. Moving forward, future work could explore integrating multi-modal imaging data and addressing challenges related to data scarcity and class imbalances to enhance diagnostic accuracy further and personalize treatment recommendations. Collaboration between computer scientists and clinicians is essential for seamless integration of AI systems into clinical workflows, ensuring reliable and safe deployment in real-world settings.

关键词： Training Accuracy Explainable AI Magnetic resonance imaging Brain tumors Data visualization Collaboration Brain modeling Data augmentation Data models

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In-situ Ultrasound Guided Closed Long Bone Shaft Fracture Treatments

In-situ Ultrasound Guided Closed Long Bone Shaft Fracture Tr...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Wenqing Yan Haowei Li Shiye Huang Long Qian Hui Ding Guangzhi Wang Zhe Zhao Tsinghua University Beijing Tsinghua Changgung Hospital Medivis Inc.

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Closed Reduction and Internal Fixation (CRIF) is the preferred treatment for closed long bone shaft fractures, where frequent X-rays imaging reduce efficiency and increase radiation exposure. Intra-operative ultrasound offers real-time, radiation-free bone imaging, but challenges orthopedic surgeons. To address this, we developed an Augmented Reality (AR) navigation system with in-situ ultrasound visualization, combining on-device infrared tool tracking via an AR headset’s depth sensor with remote rendering for accurate, low-latency images. A standard operating procedure for surgical application was also proposed. Clinical trials with six patients showed significant reduction in fluoroscopy dose and operation time.

关键词： Shafts Ultrasonic imaging Navigation Surgery X-rays Bones Rendering (computer graphics) Standards Augmented reality X-ray imaging

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Semi-Mamba: Improving Medical Image Segmentation via Semi-Automatic Mamba Network 41st

Semi-Mamba: Improving Medical Image Segmentation via Semi-Au...

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41st computer graphics international conference, CGI 2024

作者： Bi, Lei Peng, Yige Feng, David Dagan Kim, Jinman Institute of Translational Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China School of Computer Science University of Sydney CamperdownNSW Australia

ISBN: (纸本)9783031820236

Segmentation of regions of interest (ROIs) from medical images is considered as a fundamental requirement for many imaging-based clinical decision support systems. Deep learning based automatic segmentation methods have emerged as the state-of-the-art. However, their performance is heavily reliant on using a large number of annotated training dataset to encompass all the possible variations of ROIs, and when the coverage of these variations is inadequate, these methods have difficulties in segmenting the ROIs. Semi-automatic segmentation methods, which fuse user-inputs with high-level semantic image features derived from convolutional neural networks (CNNs) or vision transformers (ViTs) offer an alternative to overcome the limitations of automatic segmentation methods. Unfortunately, CNNs are restricted by the limited receptive fields that cannot capture long-range dependencies, while ViT usually rely on the self-attention mechanism to capture the global context and therefore have high computational complexity. In this study, we propose a Semi-Mamba method for semi-automatic medical image segmentation. The novelty we introduce is to leverage the capability of Mamba to efficiently capture long-range dependency and combine this together with user-clicks to segment challenging ROIs, e.g., ROIs with fuzzy boundaries and inhomogeneous textures. Our experiments with three well benchmarked medical image datasets across different modalities showed that our method consistently outperformed existing automatic and semi-automatic segmentation, which demonstrates strong generalizability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Architectural visualization using Augmented Reality

Architectural Visualization using Augmented Reality

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Nikhil Raj Hema P Menon Abhinand Satheesh Naveen Nikhil A. P Vishnuraj K. R Department of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannor Palakkad Kerala

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

In the dynamic field of architectural design, effective communication stands as a requirement for successful project realization. However, traditional 2D methods often struggle to convey the depth and essence of architectural visions to stakeholders. Augmented Reality (AR) emerges as a revolutionary tool, poised to transform architectural communication and stakeholder engagement. This work is an exploration of the transformative power of Augmented Reality in architectural design communication, employing a comprehensive methodology. By leveraging state-of-the-art technological innovations, the aim is to provide architects and designers with groundbreaking tools to translate their blueprints into immersive, dynamic experiences. Beginning with collaborative requirement analysis, conceptual design, and data acquisition, the optimization of assets for AR rendering follows suit. Augmented Reality, in tandem with Unity 3D, Figma, and specifically smartphones as AR devices are used for realization of the work. This integration fosters collaborative, immersive design experiences and propels architectural discourse into a new era of interactive visualization and communication.

关键词： Technological innovation visualization Three-dimensional displays Collaboration Transforms Rendering (computer graphics) Stakeholders Augmented reality Standards Smart phones

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2nd international conference on Applied Algorithms, ICAA 2025

2nd International Conference on Applied Algorithms, ICAA 202...

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2nd international conference on Applied Algorithms, ICAA 2025

ISBN: (纸本)9783031845420

The proceedings contain 29 papers. The special focus in this conference is on Applied Algorithms. The topics include: On the Characterization of Eulerian es-Splitting p-Matroids;approximability of Edge-Vertex Domination in Unit Disk Graphs;density Extrema of Integer Points in Standard Hexagons;Dilution with Digital Microfluidic Biochips: Unbalanced Split-Error Correction with SIMOP;social Evolution of Published Text and the Emergence of Artificial Intelligence Through Large Language Models and the Problem of Toxicity and Bias;HITgram: A Platform for Experimenting with n-Gram Language Models;Overlapping Community Detection Using Dynamic Residual Deep GCN;DeepUIR-Net: Underwater Image Restoration Using Residual-UNet with Optimized Efficient Channel Attention Network Integration;deepfake Image Detection Using Light-Weight Attention Integrated MobileNetV3 Model;AI-Driven Monitoring System for Detecting People Using Mobile Phones in Restricted Zone;integrated Analysis of Voice Patterns and Semantic Features for Emotion Recognition;Using Various Machine Learning Algorithms and xLSTM-UNet for Crop Recommendation and Disease Prediction;he-Li Graph Convolutional Neural Network: Deep and Enhanced Adaptation of Interest Modeling for Personalized Recommendation System;creation of Cartoon Face Images of Celebrities: Automation;deployable Solution for Real-Time Children Face Emotion Prediction System;vision Transformers in Evaluating Bread Edibility;MobileNet Based Fruit Classification Using UNet Generated Segmented Images: GRAD-CAM visualization;RExAS: Relation Extraction Using Adaptive Self-attention;Kontho: An AI-Driven Smart Glove for Enhanced Sign Language Communication;leveraging EfficientNetB4 Model with Multi-head Attentions for Maize Leaf Disease Detection;automated Module for Image Quality Assessment from Narrow-Banding imaging Endoscopy Cameras;does Varied Developer Interactions Cause Bugs to Be Resolved Faster? A Study of Open Source Software Ecosystems;movie Recommen

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A Survey of 3D Gaussian Splatting: Optimization Techniques, Applications, and AI-Driven Advancements

A Survey of 3D Gaussian Splatting: Optimization Techniques, ...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Santosh Reddy P Abhiram H K S Archish Department of Computer Science Engineering B.N.M. Institute of Technology Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

3D Gaussian Splatting (3DGS) has emerged as a transformative technique for real-time, high-quality 3D rendering. This paper surveys the advancements in 3DGS, focusing on optimization strategies, applications across diverse domains, and AI-driven innovations that enhance its utility. Optimization techniques, such as Mini-Splatting and sparsification frameworks, address challenges like computational overhead and memory inefficiency while maintaining visual fidelity. AI has further expanded the capabilities of 3DGS through frameworks like SceneTeller, ART3D and GaussianEditor, enabling text-to-3D generation, object-level manipulation, and artistic scene creation with multi-view consistency. Applications of 3DGS range from autonomous driving and robotics to virtual and augmented reality, highlighting its adaptability and efficiency. This survey underscores the importance of continued research to refine 3DGS’s scalability and applicability, solidifying its role at the intersection of computer graphics and artificial intelligence.

关键词： Surveys Technological innovation visualization Three-dimensional displays Scalability Rendering (computer graphics) Real-time systems Artificial intelligence Optimization Robots

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Autism Spectrum Disorder Detection Using Convolutional Neural Network

Autism Spectrum Disorder Detection Using Convolutional Neura...

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Visual Analytics and Data visualization (ICVADV), international conference on

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Neuroimaging Autism Visual analytics Face recognition Transfer learning imaging Data visualization Sensitivity and specificity Real-time systems Convolutional neural networks

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