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检索条件"任意字段=Conference on Visual Information Processing and Communication VI"

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I Hear, See, Speak & Do: Bringing Multimodal information processing to Intelligent virtual Agents for Natural Human-AI communication

I Hear, See, Speak & Do: Bringing Multimodal Information Pro...

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

作者： Ke Li Fariba Mostajeran Sebastian Rings Lucie Kruse Susanne Schmidt Michael Arz Erik Wolf Frank Steinicke Human-Computer Interaction University of Hamburg HIT Lab NZ University of Canterbury

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

In this demo paper, we present an Extended Reality (XR) framework providing a streamlined workflow for creating and interacting with intelligent virtual agents (IVAs) with multimodal information processing capabilities using commercially available artificial intelligence (AI) tools and cloud services such as large language and vision models. The system supports (i) the integration of high-quality, customizable virtual 3D human models for visual representations of IVAs and (ii) multimodal communication with generative AI-driven IVAs in immersive XR, featuring realistic human behavior simulations. Our demo showcases the enormous potential and vast design space of embodied IVAs for various XR applications.

关键词： Solid modeling visualization Three-dimensional displays Extended reality Metaverse information processing User interfaces Systems support Haptic interfaces Artificial intelligence

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vision Transformers for Mpox Detection

Vision Transformers for Mpox Detection

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International conference on Artificial Intelligence in information and communication (ICAIIC)

作者： Gelan Ayana So-Yun Park Se-Woon Choe Medical IT Convergence Engineering Kumoh National Institute of Technology Gumi Republic of Korea IT Convergence Engineering Kumoh National Institute of Technology Gumi Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Monkeypox (Mpox) has emerged as a significant public health concern, with increasing cases reported worldwide. Accurate and timely diagnosis is crucial for effective treatment and containment. Traditional diagnostic methods, such as polymerase chain reaction, though reliable, are time-consuming and require specialized equipment. Image-based detection methods have gained attention as a complementary diagnostic tool. Conventional image-based methods rely heavily on convolutional neural networks (CNNs); however, vision transformers (viTs) have emerged as a novel deep learning approach with potential benefits in medical imaging. CNN shave been the cornerstone of medical image analysis, but they come with limitations, particularly in handling complex and diverse visual data, whereas viTs, originally designed for natural language processing, have shown remarkable potential in visual tasks due to their ability to capture long-range dependencies in images. This paper explores the use of viTs, a novel deep learning architecture, in detecting Mpox. The study compares viTs with existing CNN-based methods, evaluates their performance in detecting Mpox, and discusses the implications of their use in clinical settings. Our findings suggest that viTs offer superior accuracy, making them a valuable tool in disease detection.

关键词： Computer vision visualization Accuracy Materials reliability Transformers Natural language processing Convolutional neural networks Polymers Public healthcare Medical diagnostic imaging

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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

ISBN: (纸本)9789819660049

The proceedings contain 29 papers. The special focus in this conference is on Intelligent information and Database Systems. The topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning Algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency Image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological Data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at Thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute information;continuous Recognition of Mouth Patterns in Japanese Sign Language for visual communication;Towards an UAV visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset;estimation of Distribution Algorithms with Overlapped Subpopulations;energy-Efficient Edge Query processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge Computing Through Co-design for Enhanced Data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning Systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient vietnamese Name Detection Using Highly Discriminative N-Gra

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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

ISBN: (纸本)9789819660070

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Automated Classification of Snoring and Non-Snoring Sound Signals Based on Singular Superlet Transform

Automated Classification of Snoring and Non-Snoring Sound Si...

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International conference on Signal processing and communication (ICSC)

作者： Dhanhanjay Pachori Kamal Raj Tapan Kumar Jain Department of Electronics and Communication Engineering Indian Institute of Information Technology Nagpur India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This paper proposes a new method for automated classification of snoring and non-snoring sound signals. The snoring and non-snoring sound signals have been represented using time-frequency representations (TFRs) obtained from singular superlet transform (SST). The obtained TFRs have been given as an input to various machine learning based classifiers, like convolutional neural network (CNN), visual geometry group 16-layer network (VGG-16), EfficientNet-B0, and hybrid VGG-16 models for the classification of snoring and non-snoring sound signals. From the experimental study, it has been observed that the hybrid VGG-16 with support vector machine (SVM) model has provided best performance among all the studied models with an accuracy, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 96.00%, 95.60%, 96.40%, 0.9602, 95.63%, and 0.9200 respectively. The good performance measures in automated classification of snoring and non-snoring sound signals show the effectiveness of the proposed method. The proposed method finds applications in the area of sleep disorder diagnosis and it is suitable for real-time implementation.

关键词： Support vector machines Time-frequency analysis visualization Accuracy Transforms Machine learning Signal processing Sleep apnea Convolutional neural networks Matrix converters

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mmHIU: a human-to-human interaction understanding system based on mmWave sensing

mmHIU: a human-to-human interaction understanding system bas...

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

作者： Fenglin Zhang Chenglin Wu Anfu Zhou Huadong Ma School of Computer Science Beijing University of Posts and Telecommunications

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human-to-human interaction understanding(HIU) plays a significant role in both physical and mental health of individuals in their daily lives. Nowadays, many of HIU tasks are based on visual information, which can compromise individuals’ privacy in daily life. In this paper, we propose mmHIU, a privacy-preserving HIU system based on mmWave sensing. To achieve mmHIU, we need to address two challenges: Extraction of interaction features and processing of raw data in a manner that preserves interaction features. We design multi-level feature extraction network mmHIU-STNet, and clustering-based frame normalization strategy to address these two challenges. We evaluate mmHIU on 10,811 point cloud sequences from 9 pairs of inter-actors, and results show that its accuracy reaches 90.56%, outperforming baseline methods, with a significant improvement in recognizing complex interaction behaviors.

关键词： Point cloud compression Privacy visualization Accuracy Neural networks Signal processing Feature extraction Sensors Millimeter wave communication Speech processing

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UAV-VLA: vision-Language-Action System for Large Scale Aerial Mission Generation 25

UAV-VLA: Vision-Language-Action System for Large Scale Aeria...

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ACM/IEEE International conference on Human-Robot Interaction (HRI)

作者： Oleg Sautenkov Yasheerah Yaqoot Artem Lykov Muhammad Ahsan Mustafa Grik Tadevosyan Aibek Akhmetkazy Miguel Altamirano Cabrera Mikhail Martynov Sausar Karaf Dzmitry Tsetserukou Skoltech Moscow Russia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

The UAV-VLA (visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach. Additionally, the UAV-VLA system generates all flight plans in just 5 minutes and 24 seconds, making it 6.5 times faster than an experienced human operator. The code is available here: https://***/sautenich/uav-vla

关键词： visualization Navigation Human-robot interaction Nearest neighbor methods Autonomous aerial vehicles Natural language processing Satellite images Planning Trajectory Robots

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Towards an UAV visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review

Towards an UAV Visual Navigation System Based on Map Proce...

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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

作者： Ramanauskas, Edvardas Jodlowiec, Marcin Krótkiewicz, Marek PIXPRO UAB Šiauliai Lithuania Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland School of Computer Science and Engineering International University Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819660049

This paper reviews state-of-the-art methods for UAV navigation in GNSS-denied environments, focusing on map-based spatial matching and vision-based techniques. It identifies trends and challenges in current approaches, emphasizing the integration of cartographic data, visual odometry, and data fusion. A novel methodology for identifying research gaps is presented, combining systematic literature review, qualitative and quantitative analysis with multi-criteria evaluation. The findings highlight limitations in existing methods and propose a navigation system that leverages spatial pattern matching and object recognition, integrating vision-based and GIS data. This approach addresses real-time processing challenges and offers a foundation for improving UAV navigation accuracy in GNSS-denied settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Intelligent Computing System for Breast Cancer Identification Utilizing U-Net for Digital Image Segmentation

Intelligent Computing System for Breast Cancer Identificatio...

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Intelligent Data communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Ranadheer Reddy Goli vijaya Chandra Jadala Bhavana Jamalpur Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep Learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This Intelligent Computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis.

关键词： Deep learning Image segmentation visualization Organizations Medical services Breast cancer Biomedical image processing Internet of Things Medical diagnostic imaging Tumors

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GRU Based Multiview Summarisation to obtain fast effective Key-frame

GRU Based Multiview Summarisation to obtain fast effective K...

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International conference on Computer, Electrical & communication Engineering (ICCECE)

作者： vijay Anand Kandaswamy S Joshua Kumaresan Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences. Saveetha University Chennai India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

This paper proposes a novel approach to video summarisation by introducing a feature variance model that enhances video frame extraction. In typical video record aggregation systems, a variety of image processing tasks, such as acquisition, preprocessing, segmentation, and feature extraction, should be carried out with high precision to guarantee seamless system performance. Among these, feature extraction is essential since it breaks down large amounts of visual data into more manageable, informative vectors, and the success of the system is directly impacted by how effectively this process functions. Widely Recurrent Neural Networks (RNNs) are used in supervised video summarisation processes to obtain a model for the temporal dependencies between video frames. RNN’s ability to learn long-range memory in the video is limited, and CNN often focusses on local information while losing long-term knowledge when the input sequence is lengthy. Therefore, a Gated Recurrent Unit based Neural Network (GRU-NN) for video summarisation is proposed, to classify them into keyframes and non-keyframes. Keyframes are selected for summarisation, while non keyframes are discarded or reduced. We explore various keyframe extraction techniques, analyse the performance of the proposed summarisation model, and discuss the results. Finally, we offer insights into the model’s potential improvements and its broader applications in video summarisation tasks.

关键词： Deep learning Image segmentation Computer vision Analytical models Computational modeling visual analytics System performance Feature extraction Vectors Artificial intelligence

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