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Multidimensional image morphing-fast image-based rendering of open 3D and VR environments

虚拟现实与智能硬件(中英文) 2025年第2期7卷 155-172页

作者： Simon SEIBT Bastian KUTH Bartosz von Rymon LIPINSKI Thomas CHANG Marc Erich LATOSCHIK Game Tech Laboratory Faculty of Computer ScienceNuremberg Institute of TechnologyNuremberg 90489Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and ArtsCoburg 96450Germany Human-Computer Interaction Group Institute of Computer ScienceUniversity of WuerzburgWuerzburg 97074Germany

Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains *** This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and ***,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image *** this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of *** rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image *** detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in *** addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light *** Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,*** achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering *** Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR ***,the handling of morphing artifacts in the parallax image regions remains a topic for future resea

关键词： computer graphics 3D real-time rendering computer vision Image morphing Virtual reality

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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Resilient strategies for socially compliant autonomous assistive dressing robots 23

Resilient strategies for socially compliant autonomous assis...

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1st International Symposium on Trustworthy Autonomous Systems, TAS 2023

作者： Parnell, Katie J. Merriman, Siobhan E. Getir Yaman, Sinem Plant, Katherine L. Calinescu, Radu Human Factors Engineering University of Southampton United Kingdom Department of Computer Science University of York United Kingdom

ISBN: (纸本)9798400707346

Developing resilient autonomous systems requires an interdisciplinary approach that can understand performance variability and respond to critical events when they occur. Resilience within autonomous systems must also account for social norms as well as broader ethical and legal considerations. Within this paper we outline the importance of embedding Social, Legal, Ethical, Empathetic and Cultural (SLEEC) constraints within the development of future autonomous systems. A novel methodological approach is presented that combines human factors methods with computer science techniques to generate the environmental and situational requirements in combination with a computer rule-based requirements language. This approach also provides a possible structure for capturing contextual and situational information from key stakeholders in the development of autonomous systems. This structure will enable engagement with the stakeholders with respect to key elements identified from this interdisciplinary approach in a responsible way to ensure that future autonomous systems are user centred. The approach is domain independent, but it is applied here to the case of an autonomous assistive dressing robot that aids a user in a dressing task, with a specific critical event that requires a SLEEC resilient response © 2023 ACM.

关键词： Robots

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A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4379-4387页

作者： Mishra, Vinaytosh Gupta, Kishu Saxena, Deepika Singh, Ashutosh Kumar Thumbay Institute for Ai in Healthcare Gulf Medical University Ajman United Arab Emirates National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965 Japan University of Economics and Human Sciences Department of Computer Science Warsaw01-043 Poland Indian Institute of Information Technology Bhopal Department of Computer Science and Engineering Bhopal462003 India

Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs. © 1975-2011 IEEE.

关键词： K-means clustering

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Histo-Genomic Knowledge Association for Cancer Prognosis from Histopathology Whole Slide Images

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2170-2181页

作者： Wang, Zhikang Zhang, Yumeng Xu, Yingxue Imoto, Seiya Chen, Hao Song, Jiangning Monash Biomedicine Discovery Institute Australia Monash University Department of Biochemistry and Molecular Biology Australia The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong The University of Tokyo Human Genome Center Institute of Medical Science Japan

Histo-genomic multi-modal methods have emerged as a powerful paradigm, demonstrating significant potential for cancer prognosis. However, genome sequencing, unlike histopathology imaging, is still not widely accessible in underdeveloped regions, limiting the application of these multi-modal approaches in clinical settings. To address this, we propose a novel Genome-informed Hyper-Attention Network, termed G-HANet, which is capable of effectively learning the histo-genomic associations during training to elevate uni-modal whole slide image (WSI)-based inference for the first time. Compared with the potential knowledge distillation strategy for this setting (i.e., distilling a multi-modal network to a uni-modal network), our end-to-end model is superior in training efficiency and learning cross-modal interactions. Specifically, the network comprises cross-modal associating branch (CAB) and hyper-attention survival branch (HSB). Through the genomic data reconstruction from WSIs, CAB effectively distills the associations between functional geno-types and morphological phenotypes and offers insights into the gene expression profiles in the feature space. Subsequently, HSB leverages the distilled histo-genomic associations as well as the generated morphology-based weights to achieve the hyper-attention modeling of the patients from both histopathology and genomic perspectives to improve cancer prognosis. Extensive experiments are conducted on five TCGA benchmarking datasets and the results demonstrate that G-HANet significantly outperforms the state-of-the-art WSI-based methods and achieves competitive performance with genome-based and multi-modal methods. G-HANet is expected to be explored as a useful tool by the research community to address the current bottleneck of insufficient histo-genomic data pairing in the context of cancer prognosis and precision oncology. The code is available at https://***/ZacharyWang-007/G-HANet. © 1982-2012 IEEE.

关键词： Gene expression

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Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes 10

Summit Vitals: Multi-Camera and Multi-Signal Biosensing at H...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Ke Tang, Jiankai Jiang, Zhang Wang, Yuntao Liu, Xiaojing Li, Dong Shi, Yuanchun Qinghai University Department of Computer Technology and Applications Qinghai810016 China National Key Laboratory of Human Factors Engineering Beijing100094 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

ISBN: (纸本)9798331520861

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO 2 predictions by 7. 6 % and 1 0. 6 % compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2. 5 % for SpO 2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO 2 estimation by 1 7. 8 %. © 2024 IEEE.

关键词： Photoplethysmography

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American institute of Mathematical sciences. All rights reserved.

关键词： Empirical mode decomposition

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Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

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computers, Materials & Continua 2023年第1期74卷 2195-2209页

作者： S.Rama Sree Inderjeet Kaur Alexey Tikhonov E.Laxmi Lydia Ahmed A.Thabit Zahraa H.Kareem Yousif Kerrar Yousif Ahmed Alkhayyat Department of Computer Science&Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Computer Science&Engineering Ajay Kumar Garg Engineering CollegeGhaziabadUttar PradeshIndia Department of Human Resource Management Moscow Aviation InstituteMoscowRussia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Computer Communication Engineering Al-Rafidain University CollegeBaghdadIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq College of Technical Engineering The Islamic UniversityNajafIraq

Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and *** ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with *** of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were ***,healthcare and machine learning(ML)industries are combined for determining the existence of various *** article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)*** goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical *** proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform *** addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature ***,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ***,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU *** experimental assessment of the JSODL-ASDDC model is investigated against distinct *** experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

关键词： Autism spectral disorder biomedical data deep learning feature selection hyperparameter optimization data classification machine learning

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A fast high throughput plant phenotyping system using YOLO and Chan-Vese segmentation

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Soft Computing 2024年第20期28卷 12323-12336页

作者： Jain, S. Ramesh, Dharavath Damodar Reddy, E. Rathod, Santosha Ondrasek, Gabrijel Jharkhand Dhanbad826004 India Department of Computer Science and Engineering National Institute of Technology Goa Farmagudi403401 India ICAR—Indian Institute of Rice Research Telangana Hyderabad500030 India Faculty of Agriculture University of Zagreb Zagreb10000 Croatia Department of Computer Science University of Economics and Human Sciences Warsaw Poland

Understanding plant traits is essential for decoding the behavior of various genomes and their reactions to environmental factors, paving the way for efficient and sustainable agricultural practices. Image-based plant phenotyping has become increasingly popular in modern agricultural research, effectively analyzing large-scale plant data. This study introduces a new high-throughput plant phenotyping system designed to examine plant growth patterns using segmentation analysis. This system consists of two main components: (i) A plant detector module that identifies individual plants within a high-throughput imaging setup, utilizing the Tiny-YOLOv4 (You Only Look Once) architecture. (ii) A segmentation module that accurately outlines the identified plants using the Chan-Vese segmentation algorithm. We tested our approach using top-view RGB tray images of the ‘Arabidopsis Thaliana’ plant species. The plant detector module achieved an impressive localization accuracy of 96.4% and an average Intersection over Union (IoU) of 77.42%. Additionally, the segmentation module demonstrated strong performance with dice and Jaccard scores of 0.95 and 0.91, respectively. These results highlight the system’s capability to define plant boundaries accurately. Our findings affirm the effectiveness of our high-throughput plant phenotyping system and underscore the importance of employing advanced computer vision techniques for precise plant trait analysis. These technological advancements promise to boost agricultural productivity, advance genetic research, and promote environmental sustainability in plant biology and agriculture. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Agriculture

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computer vision-based hybrid efficient convolution for isolated dynamic sign language recognition

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Neural Computing and Applications 2024年第32期36卷 19951-19966页

作者： Chowdhury, Prothoma Khan Oyshe, Kabiratun Ummi Rahaman, Muhammad Aminur Debnath, Tanoy Rahman, Anichur Kumar, Neeraj Department of Computer Science and Engineering Green University of Bangladesh 220/D Begum Rokeya Sarani Purbachal American City Kanchon Dhaka1207 Bangladesh Constituent Institute of the University of Dhaka Kohinoor gate Dhaka-Aricha Hwy Nayarhat Savar Dhaka1350 Bangladesh Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India University of Economics and Human Sciences WARSAW Poland

Isolated dynamic sign language recognition (IDSLR) has the potential to change accessibility and inclusion by enabling speech and/or hearing-impaired people to engage more completely in a variety of spheres of life, including social interactions, work, and more. IDSLR is a challenging task due to considering a sequence of image frame analysis with multiple linguistic features for a single gesture in cluttered backgrounds and an illumination variation environment. We have proposed a Hybrid Efficient Convolution (HEC) model that ensembles EfficientNet-B3 and a few modified layers as an alternative to traditional machine learning techniques with improved performances in cluttered backgrounds with illumination variation environments. The architecture of the HCE integrates pre-trained layers of EfficientNet-B3 loaded with customized weights and a new custom dense layer featuring 256 units, followed by batch normalization, dropout, and the final output layer. To enhance the robustness of the system, we employed the augmentation technique during pre-processing. Then, the system executes channel-wise feature transformation through point-wise convolution that reduces the computational complexity and increases the accuracy. The updated dense layer with 256 units processes the output from the standard EfficientNet-B3, shaping the model into a hybrid form to achieve better performance. We have created our own gesture dataset, called "BdSL_OPA_23_GESTURES," which consists of 6000 video clips of 100 isolated dynamic Bangla Sign Language words, with 60 videos for each word from 20 different people in the cluttered background with illumination variation environments to train and evaluate the performances of the proposed model. We have considered 80% of the total dataset for training purpose, while the remaining 20% is dedicated to testing and validation. In a small number of epochs, our proposed HEC model achieves a superior accuracy of 93.17% on our created "BdSL_OPA_23_GESTURES"

关键词： Convolution

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