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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

共 54092 条记录，以下是371-380 订阅

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Augmenting Murals: Creating Playful AR Experiences for Wall Art Exhibitions

Augmenting Murals: Creating Playful AR Experiences for Wall ...

引用

2025 ieee conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Bauer, Axel Kocur, Martin Hagler, Juergen Uas Upper Austria Hagenberg Campus Austria

ISBN: (纸本)9798331514846

Outdoor art exhibitions in natural settings offer a popular environment for people to engage with art. With the rapid advancements in augmented reality and mixed reality, immersive technologies and digital augmentations are transforming how people experience art by incorporating animations and interactive elements. However, marker-based augmented reality (AR) approaches, though commonly used, present limitations in large outdoor exhibitions, including labour-intensive setups and restricted exploration freedom for users. To address these challenges, we propose a markerless AR approach utilizing Geospatial tracking, which appears promising for enhancing large-scale outdoor art exhibitions. In this paper, we present the development of a mobile AR application designed to augment murals with dynamic animations and interactive features. We explore various interaction concepts, demonstrating how this technology can be effectively utilized to create innovative, user-centered experiences in outdoor art settings. © 2025 ieee.

关键词： augmented reality murals outdoor art exhibitions visualization

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MS2Mesh-XR: Multi-Modal Sketch-to-Mesh Generation in XR Environments

MS2Mesh-XR: Multi-Modal Sketch-to-Mesh Generation in XR Envi...

引用

ieee International conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Yuqi Tong Yue Qiu Ruiyang Li Shi Qiu Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong HKSAR China Institute of Medical Intelligence and XR The Chinese University of Hong Kong HKSAR China

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

We present MS2Mesh-XR, a novel multimodal sketch-to-mesh generation pipeline that enables users to create realistic 3D objects in extended reality (XR) environments using hand-drawn sketches assisted by voice inputs. In specific, users can intuitively sketch objects using natural hand movements in mid-air within a virtual environment. By integrating voice inputs, we devise ControlNet to infer realistic images based on the drawn sketches and interpreted text prompts. Users can then review and select their preferred image, which is subsequently reconstructed into a detailed 3D mesh using the Convolutional Reconstruction Model. In particular, our proposed pipeline can generate a high-quality 3D mesh in less than 20 seconds, allowing for immersive visualization and manipulation in runtime XR scenes. We demonstrate the practicability of our pipeline through two use cases in XR settings. By leveraging natural user inputs and cutting-edge generative AI capabilities, our approach can significantly facilitate XR-based creative production and enhance user experiences. Our code and demo will be available at: https://***/MS2Mesh-XR/.

关键词： Convolutional codes Solid modeling visualization Adaptation models Three-dimensional displays Generative AI Pipelines Virtual environments Real-time systems Image reconstruction

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Trustworthy Reputation for Federated Learning Leveraging Blockchain: A Demonstration

Trustworthy Reputation for Federated Learning Leveraging Blo...

引用

ieee conference on Wireless Communications and Networking

作者： Farhana Javed Josep Mangues-Bafalluy Engin Zeydan Luis Blanco Services as networkS (SaS) Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA) Castelldefels Spain Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA) Space and Resilient Communications and Systems (SRCOM) Castelldefels Spain

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The inherent virtualization characteristics of 5G, 6G, and subsequent generations (xG) facilitate the modularity of components and their interfaces, leading to an increase in the number of stakeholders and necessitating more complex administrative relationships. In this demonstration, we present an open-source Decentralized Application (DApp) integrated with smart contracts deployed on a live Polygon testnet to support trustworthy Federated Learning (FL). FL enables clients to conduct local training and updates, while a central aggregation server processes these inputs for global FL training. We introduce a blockchain-based reputation system within such FL model to facilitate collaborative training of Machine Learning (ML) models. The deployed smart contracts on the Layer 2 Polygon blockchain calculate and store reputation scores on-chain for each FL client based on their contribution. This demonstration presents a visualization of the blockchain network parameters during the execution of blockchain-enabled smart contracts. The DApp offers an interactive interface for client registration, performance parameter submission, and reputation score calculation. We use Alchemy's dashboard for real-time monitoring of blockchain transactions and smart contract interactions. The open-source implementation is publicly available 1 1 https://***/farhanajaved/bc-fl-demo-l2 and the video of the demo is available at 2 2 https://***/B9J1gxjoKdM.

关键词： Training visualization Federated learning Smart contracts Collaboration Decentralized applications Real-time systems Blockchains Stakeholders Testing

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Extended Reality: As an Educational Tool for Understanding Breast Composition and Radiology

Extended Reality: As an Educational Tool for Understanding B...

引用

ieee International conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Ziyuan Zhou Faraz Janan Imperial College London London UK

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

This paper introduces a novel breast density estimation method in extended reality (XR), presenting a new approach with significant potential for biomedical education and training. It involves a comparative analysis between density estimations derived from 2D mammograms and volumetric scores obtained from MRI scans projected in a 3D virtual space. The software, developed using the Unity Engine (UE) and Microsoft's Mixed Reality Toolkit (MRTK), enables bilateral density estimation through two distinct methods: 1) manual threshold-based segmentation and 2) opacity-based volumetric segmentation. The results exhibit a strong positive linear correlation between scores computed from the 3D volume and those derived from the central projections of 2D MRIs, with Pearson Product-Moment Correlation (PMCC) values of 0.939 and 0.917 for the right and left breasts, respectively, using manual threshold segmentation. Similarly, opacity-based segmentation yields PMCC values of 0.924 and 0.864. Breast density estimations derived from MRI central projections indicate a lower risk in comparison to mammographic assessments, highlighting the advantages of volumetric analysis. Moreover, instances of global asymmetry among bilateral breasts suggest tumor laterality and indicative localization. These findings underscore the potential of XR as a transformative tool for enhancing breast cancer risk assessment, visualization, and detection, while also offering innovative educational applications for biomedical training and diagnostics. This research sets a foundation for integrating XR into clinical education and the development of advanced CADe/i systems

关键词： Training visualization Three-dimensional displays Correlation Extended reality Magnetic resonance imaging Estimation Manuals Software Tumors

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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

引用

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

作者： Radhakrishnan, Venkateswaran Rodda, Vijayakumari Sowmiya, S. Sarker, Abdur Rahman Palanikumar, R. Asokan, R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Dhofar Oman Krishna University Department of Computer Science Rudravaram Machilipatnam521004 India Sri Eshwar College of Engineering Coimbatore641202 India Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India Kongunadu College of Engineering and Technology Department of Ece Tamil Nadu Trichy India

ISBN: (纸本)9798331521394

With the rapid advancements in artificial intelligence, deep-learning-based cryptanalysis has emerged as a promising approach to evaluating the security of modern encryption algorithms. Traditional cryptanalysis methods, such as linear and differential attacks, often require extensive mathematical modelling and computational effort. In contrast, deep learning enables automated feature extraction and pattern recognition, potentially identifying cryptographic weaknesses more efficiently. This paper presents an advanced neural network-based cryptanalysis framework targeting modern encryption schemes, including AES, RSA, and lightweight block ciphers. We employ deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformer models to analyse ciphertext-plaintext relationships and recover cryptographic keys. Additionally, we explore the impact of data preprocessing, hyperparameter optimization, and model interpretability on cryptanalysis accuracy. Our experiments demonstrate that deep learning can achieve significant improvements in key recovery probability compared to conventional approaches. However, we also highlight the computational challenges and practical limitations of deep-learning-based cryptanalysis. This study provides insights into the applicability of AI-driven techniques in cryptanalysis and their implications for cryptographic security. © 2025 ieee.

关键词： Convolutional neural networks

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A Flexible and Ultrasensitive Artificial Compound Eye Using Bionic Micro-Lens Array for Drone Vision

A Flexible and Ultrasensitive Artificial Compound Eye Using ...

引用

ieee International conference on Micro Electro Mechanical Systems

作者： Jiachuang Wang Fangyu Zhao Wenyuan Liu Nan Qin Tiger H. Tao State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China School of Graduate Study University of Chinese Academy of Sciences Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China Neuroxess Co. Ltd. (Jiangxi) Nanchang China Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China Tianqiao and Chrissy Chen Institute for Translational Research China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

We report a bionic artificial compound eye based on flexible quantum dots film and micro electromechanical system (MEMS) fabrication process. The device integrates organic photodetector and micro-lens array, featuring the high-speed response, ultrathin thickness and extensive curvature radius. Our artificial eye exhibits significant discriminative detection to different targets with varying shapes and distances. The insect scale biomimetic compound eye provides a novel precision integration route for flexible optoelectronic devices and micro-optics, showing great potential applications in drone vision and bionic robot navigation.

关键词： Micromechanical devices visualization Sensitivity Navigation Shape Biomimetics Robot sensing systems Photodetectors Compounds Drones

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Enhancing Automotive Usability Testing and User Experience: Insights from Virtual Reality and Eye Tracking Integration

Enhancing Automotive Usability Testing and User Experience: ...

引用

ieee International conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Catharina Ramos Rafael Miguez Felipe Leão Daniela Rodrigues Matheus Demetrescu Fábio Sandrin Márcio Alfonso Rodrigo Leite Ingrid Winkler Global Automotive Salvador Brazil SENAI CIMATEC Salvador Brazil SENAI CIMATEC Studio Salvador Brazil Research and Development GWM São Paulo Brazil UX GWM São Paulo Brazil INCITE INDUSTRIA 4.0 SENAI CIMATEC AKCIT UFG Salvador Brazil

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

In the context of Industry 5.0, the integration of virtual reality and eye tracking has become important in the transformation of automotive manufacturing processes. The research demonstrates the application of virtual reality and the use of eye tracking technology in identifying critical areas of visual attention in a vehicle, as well as providing quantitative data about this visualization in order to contribute to a more user-centered vehicle design. This study examines the combination of virtual reality and eye tracking in usability testing and user experience research, providing deeper insights into user behavior and interaction in the automotive industry. Following a Design Science Research approach, an immersive environment was developed using Unreal Engine, the Pico 4 Enterprise head-mounted display and the Cognitive3D tool. Throughout the work we detail the construction process, the use of the tools used and the potential of this application in the automotive industry, mainly in the vehicle development process and user testing phase.

关键词： Industries visualization Manufacturing processes Head-mounted displays Virtual reality Gaze tracking User experience Usability Automotive engineering Testing

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Deformable Attention-Based Edge-Aware Network for Single Image Super-Resolution

Deformable Attention-Based Edge-Aware Network for Single Ima...

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

作者： Ju Zhang Baojiang Zhong Kai-Kuang Ma School of Computer Science and Technology Soochow University Suzhou China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately reconstructing object edges is a key challenge in single image super-resolution (SISR), as it greatly influences our visual perception of image quality. To address this fundamental issue, we propose a novel SISR approach named the deformable attention-based edge-aware (DAE) network. The DAE network features a deformable attention block that dynamically adjusts attention weights to align with edge structures, thereby improving edge awareness and reconstruction quality. Moreover, our network incorporates a multi-patterns window block that captures fine-grained edge details and enhances information flow across network layers. This combination results in visually superior SISR outputs. Extensive experiments and comparisons with the state-of-the-art methods demonstrate that our DAE network excels in both quantitative metrics and visual quality.

关键词： Measurement visualization Image edge detection Superresolution Stacking Refining Speech processing Image reconstruction Signal resolution Visual perception

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Multi-modal Entity Alignment under Imbalanced Visual Modality information

Multi-modal Entity Alignment under Imbalanced Visual Modalit...

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

作者： Xin Zhang Yu Liu Shimin Shan School of Software Dalian University of Technology Dalian China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-modal entity alignment (EA) is an important extension of the EA task. Existing multi-modal EA methods use randomly generated embeddings to supplement the missing image features of entities, ignoring the impact of correlation between entities and images. In this paper, we propose IVMEA, a multi-modal EA framework under imbalanced visual modality information. Specifically, IVMEA first establishes a mapping network from semantic features to image features, and then completes the training of the mapping network in terms of both alignment loss and cross-modal loss to obtain semantic-aware image embeddings for multi-modal EA. Finally, the multi-modal EA task is completed based on the similarities of the multi-modal feature embeddings. Experimental results show that our proposed IVMEA achieves the state-of-the-art (SOTA) performance on three cross-lingual datasets with limited parameters and runtime in various imbalanced visual modality scenarios.

关键词： Training visualization Runtime Correlation Attention mechanisms Semantics Signal processing algorithms Signal processing Speech processing Computational complexity

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MambaNext: An Enhanced Backbone Network with Focus Linear Attention

MambaNext: An Enhanced Backbone Network with Focus Linear At...

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

作者： Dafeng Zhang Shizhuo Liu Samsung Research China - Beijing (SRC-B) Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In response to the limitations of current linear attention models, such as Vision Mamba, which fail to mimic the human visual system’s ability to focus on objects and then shift attention to the surrounding context when ambiguity arises, we introduce the MambaNext model. This novel backbone network incorporates two key innovations: the Focus Linear Attention Module (FLAM) and the Star Fusion strategy. FLAM is designed to enhance object recognition by emulating the human visual system’s focused center, thereby reducing the interference from background elements. On the other hand, Star Fusion acts as a unique operation that utilizes global information as an attention map to guide local information toward more relevant features. Additionally, it also implicitly increases the feature dimensions to improve linear separability. The experimental results demonstrate that our MambaNext has achieved state-of-the-art performance across multiple computer vision tasks including classification, detection, and segmentation, outperforming existing Vision Mamba methods.

关键词： visualization Image segmentation Adaptation models Technological innovation Stars Interference Visual systems Signal processing Speech processing Context modeling

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