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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231-240 订阅

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TD-GS: Few-shot Object View Synthesis via Task-Disentangled 3D Gaussian Splatting

TD-GS: Few-shot Object View Synthesis via Task-Disentangled ...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jialin Wu Ying Liu Xu Wang Xiaohao Zhang Zhuo Tang Ruihui Li College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D Gaussian Splatting (3D-GS) has exhibited impressive progress in novel view synthesis. When given the sparse views, its performance degrades severely, causing many problems like novel views collapse and excessive floaters. Many recent methods take into account fitting input views, inferring missing scene information and optimizing the final scene representation, all through a single stage. After revisiting the task, we propose a novel framework, Task-Disentangled 3D Gaussian Splatting, abbreviated to TD-GS. It splits the sparse views synthesis task into two subtasks: (i) Dense Generation. (ii) Enhanced Synthesis. In the subtask of Dense Generation, we estimate dense views from sparse input. Then in the subtask of Enhanced Synthesis, both the dense views and the sparse input participate in the training of Gaussians to obtain the final Gaussian representation of the scene. In the process of completing the first subtask, we carefully design Gaussian Cloud Denoising to directly edit 3D Gaussians. Also, we introduce two regularization methods to guide the geometric optimization towards an optimal solution. The purpose is to estimate more reliable outputs. Many experiments have validated that our TD-GS outperforms other state-of-the-art methods.

关键词： Training visualization Three-dimensional displays Noise reduction Signal processing Rendering (computer graphics) Real-time systems Reliability Speech processing Optimization

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Enhancing Perceptual Quality of Images using Deep Residual U-Net and PatchGAN Discriminator

Enhancing Perceptual Quality of Images using Deep Residual U...

引用

international conference on New Trends in Computing Sciences (ICTCS)

作者： Satvik Raghav Sathwik Narkedimilli Prasanth Ayitapu Raja Karthikeya Lalitha Sreeram Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science Engineering Indian Institute of Information Technology (IIIT) Dharwad India

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

This paper presents a novel approach for perceptually aware image enhancement utilizing a deep residual U-Net generator combined with a Patch-based Generative Adversarial Network (PatchGAN) discriminator. Our method incorporates a perceptual loss function based on a pre-trained VGG16 network, ensuring that enhanced images exhibit improved color sharpness, clarity, and retention of critical high-level features. Experiments show improved structural similarity, with SSIM of 0.9270 and FSIM of 0.9998. These results indicate both perceptual and structural enhancement, while the model efficiently generates high-quality images with minimal training. Our method consistently outperforms conventional approaches in visual appeal and computational efficiency, making it relevant for real-world applications such as radiology, self-driving vehicles, and low-light photography. Our implementation is open-sourced and available here: https://***/bp8zsl.

关键词： Training visualization Technological innovation Smart cities Service robots Computational modeling Surveillance Image enhancement Biomedical imaging Sports

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IoT Mobile Application for Alzheimer's Disease Caregivers

IoT Mobile Application for Alzheimer's Disease Caregivers

引用

IEEE international conference on Consumer Electronics (ICCE)

作者： Logan Pasternak Srijohn Roy Andrew Farag Monica Rubens Ameek Chadha Sasan Haghani Electrical and Computer Engineering Rutgers University Piscataway NJ USA

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Caring for a friend or family member diagnosed with Alzheimer's disease poses significant challenges due to the necessity for continuous supervision, attentive care, and support. Given the exorbitant expenses associated with professional nursing care, many families with limited financial resources find themselves relying on the unpaid assistance of friends and relatives to fulfill these caregiving responsibilities. Consequently, there is pressing demand for cost-effective health monitoring solutions and systems geared towards enhancing the quality of life for both patients and caregivers alike. This paper addresses this critical need through the development of an iOS mobile application. The application seamlessly integrates various Internet of Things (IoT) technologies, strategically designed to alleviate the burdens of caregiving. Central to its functionality, the application encompasses four key features: medication management, visual short-term memory assessment, facial recognition, and patient location tracking.

关键词： visualization Face recognition Memory management Pressing Medical services Mobile applications Internet of Things Alzheimer's disease Monitoring Medical diagnostic imaging

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Predicting local fMRI activations from EEG: a Feasibility Study Using Both Classical and Modern Machine Learning Pipelines

Predicting local fMRI activations from EEG: a Feasibility St...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tomer Amit Taly Markovits Guy Gurevitch Talma Hendler Lior Wolf The School of Computer Science Tel-Aviv University Tel Aviv Israel Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

fMRI’s clinical use is limited by cost, while EEG is more accessible but lacks spatial detail and deep brain coverage. Research aims to predict deep brain activations from combined fMRI and EEG data. We compare classical machine learning and a CNN-transformer pipeline for this mapping across multiple brain regions. As we show, in the first dataset, which is heavily tilted toward visual perception, the activations in the Hippocampus cannot be recovered reliably from EEG, using either pipeline. However, in other regions, predictability is much higher, and in those cases, the deep learning pipeline obtains better predictions. In a second dataset that is based on musical feedback while the visual is blocked, both pipelines yield improved results in the Hippocampus.

关键词： Deep learning visualization Pipelines Time series analysis Functional magnetic resonance imaging Electroencephalography Reliability Speech processing Hippocampus Visual perception

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Comprehensive Assistive Language Model (CALM) for Neurodivergent Child Language Acquisition

Comprehensive Assistive Language Model (CALM) for Neurodiver...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), international conference on

作者： Shivranjani Balaji S. Vigneshwari Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The Comprehensive Assistance Language Model–CALM, is a Natural Language Processing framework designed to support consistent language acquisition in children and aid their phonetic, semantic, and syntactic understanding. The model is intentionally designed to engage the child through empathy, task simplification, age-appropriate adaptation and trait-based multimodality. Most neurodivergent children struggle with language acquisition through traditional learning methods and develop a language gap that continues to grow into their adolescence and adulthood. Timely intervention through immersive learning approaches can mitigate or prevent these challenges. CALM integrates two components-a prompt-tuned LLM and a trained transformer model. The inclusion of a user report prepared by the prompt-tuned LLM on traits, age, caretaker concerns and detected condition understanding, increases the response quality and the child’s learning engagement. Layering a transformer model for contextual refinement and grammar correction over the LLM-generated response ensures coherence and improves relevance in answering the child’s query. CALM uses emojis and graphics as part of the generated text to enhance visual learning and also has the option of speech input/output through the incorporated LLM. CALM has achieved meaningful results when measured using evaluation metrics such as accuracy and response time, as well as manually validated characteristics including empathy, bias, engagement, and relevance. CALM effectively simplifies tasks, visualises learning and aligns solutions to the child’s interests.

关键词： Adaptation models visualization Accuracy Phonetics Transformers Natural language processing Time measurement Time factors Usability Tuning

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Dynamic Partitioning of 3D Scenes into User-Bounding Boxes for Immersive Metaverse Experience

Dynamic Partitioning of 3D Scenes into User-Bounding Boxes f...

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Disruptive Technologies (ICDT), international conference on

作者： Deep Kumar Sneha Chauhan Lalit Kumar Awasthi Department of CSE National Institute of Technology Uttarakhand India Department of CSIT KIET Group of Institutions Ghaziabad NCR India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The partitioning of 3-dimensional environments into user-specific bounding boxes, accompanied by immersive audio and visual experiences, presents a significant challenge in virtual and augmented reality systems. This study examines methods for the adaptive segmentation and management of virtual environments to improve user interaction and immersion. The system utilizes spatial partitioning algorithms such as octrees, Voronoi diagrams, and bounding volume hierarchies to dynamically allocate regions of 3D space according to the needs of individual users. Immersive audio experiences utilize directional sound rendering via Head-Related Transfer Functions and dynamic audio zones, whereas adaptive visual rendering incorporates techniques like level of detail, frustum culling, and real-time lighting adjustments. The proposed framework integrates AI-driven personalization, procedural content generation, and gesture-based interaction to establish context-aware and responsive environments. Synchronization among networked devices enables smooth transitions in multi-user environments, supported by edge computing and consensus algorithms. This research addresses the technical complexities of real-time dynamic partitioning and immersive multimodal experiences to enhance user engagement and redefine interaction paradigms in virtual spaces. The proposed solutions exhibit scalability and adaptability for use in gaming, training simulations, and collaborative metaverse environments.

关键词： Training visualization Three-dimensional displays Metaverse Scalability Transfer functions Rendering (computer graphics) Real-time systems Synchronization Immersive audio

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Application of Visual Communication in the Three-Dimensional Image Design of Interior Architecture

Application of Visual Communication in the Three-Dimensional...

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Intelligent Systems and Computational Networks (ICISCN), international conference on

作者： Danqiong Liu Shengke Zhou Qian Sun Yuncheng Vocational and Technical University Yuncheng China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

This study explores the application of visual communication design in interior architectural three-dimensional image design, aiming to solve the problems of unclear definition of indoor LoD, low modeling efficiency and insufficient integration of indoor space information in smart city construction. The research method is based on visual communication design, which enhances the expressiveness and user experience of interior design through the integration of visual elements such as graphics, color and typography. First, this study introduces the application of visual communication design in automated modeling, using visual recognition technology to automatically generate three-dimensional models from floor plans, which significantly improves modeling efficiency. Secondly, the application of visual communication design in indoor navigation and positioning services is studied to provide more intuitive navigation instructions through augmented reality technology, thereby improving the accuracy and user experience of indoor navigation. Experimental results show that the visual communication method performs well in LoD evaluation under different view conditions, with significant advantages over traditional methods in terms of geometric accuracy, texture quality, element integrity, and indoor LoD similarity. In addition, the application of visual communication improves the efficiency of indoor 3D image design and improves the integration of indoor space information. The evaluation of the rationality of element layout reaches 94.15%, providing a more effective indoor architectural 3D image design solution for smart city construction. plan.

关键词： Solid modeling visualization Three-dimensional displays Accuracy Visual communication Smart cities Indoor navigation computer architecture User experience Parametric statistics

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Creating Immersive Flight Simulators Using NVIDIA Omniverse and X-Plane: Guidelines for Beginner Designers

Creating Immersive Flight Simulators Using NVIDIA Omniverse ...

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IEEE international conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Daniela Rodrigues Matheus Demetrescu Fábio Sandrin Andrieli Souza Helosman Valente Pedro Mario Cruz E Silva Yiyu Cai Ingrid Winkler Global Automotive Salvador Brazil SENAI CIMATEC Salvador Brazil EMBRAER São Paulo Brazil NVIDIA Rio de Janeiro Brazil Nanyang Technological University Singapore INCITE INDUSTRIA 4.0 SENAI CIMATEC AKCIT UFG Salvador Brazil

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

Using virtual reality authoring tools can be challenging for beginners and professionals without programming or 3D modeling experience. In this context, this work provides guide-lines for beginners to create an immersive flight simulator using the NVIDIA Omniverse and X-Plane platforms. We followed the design science research paradigm and conducted an exploratory study, utilizing the NVIDIA Omniverse, X-Plane Maker, Embraer Tucano EMB-314 and EMB-175 models, and Meta Quest 3 to develop the immersive experience. During development, we faced challenges such as rendering distortions, stitching issues, inadequate light exposure, and scaling of virtual models. To address these issues, we developed recommendations such as: optimizing lighting to avoid overexposure, adjusting textures and materials to eliminate distortions, ensuring appropriate scale of models for comfort and realism, and prioritizing elements such as immersion, intuitive navigation, and visual quality to create an effective and engaging simulation experience.

关键词： Solid modeling visualization Three-dimensional displays Navigation Lighting Immersive experience Distortion Rendering (computer graphics) Programming profession Guidelines

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Enhancing Alzheimer's Disease Detection Using Optimized Attribute Selection and Random Forest Classifier for Improved Accuracy

Enhancing Alzheimer's Disease Detection Using Optimized Attr...

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

作者： A. Sathiya CH Hussaian Basha Vasanth. S Jasphin Jeni Sharmila P Prabakaran S R. Indhumathi Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering (Autonomous) Karur Department of Electrical and Electronics Engineering SR University Warangal Department of Electronics and communication Engineering Rajalakshmi Institute of Technology Chennai Department of computer science and Engineering Chennai Institute of Technology Chennai Department of Electronics and communication Engineering CMS College of Engineering and Technology Coimbatore Department of Biomedical Engineering Velalar College of Engineering and Technology Erode

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This research aims at improving the identification of Alzheimer's dementia by using the optimized attribute selection method and preliminary diagnosis data-driven learning algorithm, namely Random Forest Classifier. Alzheimer's is a progressive neurological disorder exhibiting barriers in preliminary diagnosis primarily due to the high density and intricate nature of medical information. In this study, attribute selection techniques are used to minimize the data dimensionality, by selecting only key features for classification with low computational complexity and improved model accuracy. The Random Forest Classifier is used because it is accurate, can process big data and because of its diverse uses. Through selecting the most relevant features of the model, it provides a prominent level of precise and acute identification of the early stage of Alzheimer's and could significantly enhance the medical treatment effects. The outcomes from this study indicate that feature optimization can be used interchangeably with preliminary diagnosis data-driven learning algorithms for accurate disease prediction.

关键词： Accuracy Computational modeling Volume measurement Visual analytics Classification algorithms Alzheimer's disease Slabs Random forests Medical diagnostic imaging Monitoring

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Detection of Bone Cancer using Machine Learning: A Multi-Model Approach

Detection of Bone Cancer using Machine Learning: A Multi-Mod...

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Electronics and Renewable Systems (ICEARS), international conference on

作者： Pushya Saie Raag Enuga Sanjali Singh V Santhi School of Computer Science and Engg. Vellore Institute of Technology Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Bone cancer diagnosis remains a critical challenge due to the intricate nature of medical imaging data and the need for precise interpretation. This study introduces a machine learning framework leveraging Convolutional Neural Networks (CNNs) and transfer learning to automate bone cancer detection. We benchmarked four models—ResNet50, ResNet101, EfficientNet-B7, and a custom lightweight CNN—on a dataset comprising over 8,000 X-ray images. Among these, ResNet101 demonstrated the highest diagnostic performance, achieving an accuracy of 94.46 %, which surpasses ResNet50 by 4.26 %, EfficientNet-B7 by 32.46 %, and the custom CNN by 7.39 %. The custom CNN, designed for low-power scenarios, achieved a commendable accuracy of 87.07 %, highlighting its suitability for resource-constrained environments. Data augmentation techniques, including random flipping, rotation (±20°), zooming (0.8 to 1.2), and contrast adjustment (±20 %), enhanced model generalization. Grad-CAM visualizations offered interpretable insights into model predictions, empowering clinicians with clear decision-making frameworks. Comparative analysis with existing literature underscores significant advancements, identifies remaining gaps, and suggests opportunities for future research. This work highlights the promise of CNN-based solutions in advancing medical diagnostics and provides a roadmap for deploying efficient AI systems in clinical practice.

关键词： Accuracy Transfer learning Decision making Data visualization Bones Data models Convolutional neural networks X-ray imaging Cancer Residual neural networks

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