{"title":"Selective sampling with Gromov–Hausdorff metric: Efficient dense-shape correspondence via Confidence-based sample consensus","authors":"Dvir Ginzburg, Dan Raviv","doi":"10.1016/j.vrih.2023.08.007","DOIUrl":"https://doi.org/10.1016/j.vrih.2023.08.007","url":null,"abstract":"<div><h3>Background</h3><p>Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” sce- nario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used.</p></div><div><h3>Methods</h3><p>A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the <em>Gromov–Hausdorff distance metric</em> was used to select the points with the maximal alignment score displaying most confidence.</p></div><div><h3>Results</h3><p>The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods.</p></div><div><h3>Conclusions</h3><p>The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 1","pages":"Pages 30-42"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S209657962300058X/pdf?md5=0d72c2ce81fa69712b18835a2698ec47&pid=1-s2.0-S209657962300058X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mingjian Li , Younhyun Jung , Michael Fulham , Jinman Kim
{"title":"Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study","authors":"Mingjian Li , Younhyun Jung , Michael Fulham , Jinman Kim","doi":"10.1016/j.vrih.2023.08.005","DOIUrl":"https://doi.org/10.1016/j.vrih.2023.08.005","url":null,"abstract":"<div><h3>Background</h3><p>A medical content-based image retrieval (CBIR) system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image. CBIR is widely used in evidence- based diagnosis, teaching, and research. Although the retrieval accuracy has largely improved, there has been limited development toward visualizing important image features that indicate the similarity of retrieved images. Despite the prevalence of3D volumetric data in medical imaging such as computed tomography (CT), current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images. Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information, including the size, shape, and spatial relations of multiple structures. This process is time-consuming and reliant on users’ experience.</p></div><div><h3>Methods</h3><p>In this study, we proposed an importance-aware 3D volume visualization method. The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process. We then integrated the proposed visualization into a CBIR system, thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.</p></div><div><h3>Results</h3><p>Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography (PET- CT) images of a non-small cell lung cancer dataset.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 1","pages":"Pages 71-81"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000566/pdf?md5=771df0097b94f27ef3ca76e8f800722b&pid=1-s2.0-S2096579623000566-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Effective data transmission through energy-efficient clus- tering and Fuzzy-Based IDS routing approach in WSNs","authors":"Saziya Tabbassum (Research Scholar) , Rajesh Kumar Pathak (Vice Chancellor)","doi":"10.1016/j.vrih.2022.10.002","DOIUrl":"https://doi.org/10.1016/j.vrih.2022.10.002","url":null,"abstract":"<div><p>Wireless sensor networks (WSN) gather information and sense information samples in a certain region and communicate these readings to a base station (BS). Energy efficiency is considered a major design issue in the WSNs, and can be addressed using clustering and routing techniques. Information is sent from the source to the BS via routing procedures. However, these routing protocols must ensure that packets are delivered securely, guar- anteeing that neither adversaries nor unauthentic individuals have access to the sent information. Secure data transfer is intended to protect the data from illegal access, damage, or disruption. Thus, in the proposed model, secure data transmission is developed in an energy-effective manner. A low-energy adaptive clustering hierarchy (LEACH) is developed to efficiently transfer the data. For the intrusion detection systems (IDS), Fuzzy logic and artificial neural networks (ANNs) are proposed. Initially, the nodes were randomly placed in the network and initialized to gather information. To ensure fair energy dissipation between the nodes, LEACH randomly chooses cluster heads (CHs) and allocates this role to the various nodes based on a round-robin management mechanism. The intrusion-detection procedure was then utilized to determine whether intruders were present in the network. Within the WSN, a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes. Subsequently, an ANN was employed to distinguish the harmful nodes from suspicious nodes. The effectiveness of the proposed approach was validated using metrics that attained 97% accuracy, 97% specificity, and 97% sensitivity of 95%. Thus, it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 1","pages":"Pages 1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579622001139/pdf?md5=33169ccdb2fe0c8e8a08f569df224af6&pid=1-s2.0-S2096579622001139-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fei Li , Zhibao Qin , Kai Qian , Shaojun Liang , Chengli Li , Yonghang Tai
{"title":"Personalized assessment and training of neurosurgical skills in virtual reality: An interpretable machine learning approach","authors":"Fei Li , Zhibao Qin , Kai Qian , Shaojun Liang , Chengli Li , Yonghang Tai","doi":"10.1016/j.vrih.2023.08.001","DOIUrl":"https://doi.org/10.1016/j.vrih.2023.08.001","url":null,"abstract":"<div><h3>Background</h3><p>Virtual reality technology has been widely used in surgical simulators, providing new opportunities for assessing and training surgical skills. Machine learning algorithms are commonly used to analyze and evaluate the performance of participants. However, their interpretability limits the personalization of the training for individual participants.</p></div><div><h3>Methods</h3><p>Seventy-nine participants were recruited and divided into three groups based on their skill level in intracranial tumor resection. Data on the use of surgical tools were collected using a surgical simulator. Feature selection was performed using the Minimum Redundancy Maximum Relevance and SVM-RFE algorithms to obtain the final metrics for training the machine learning model. Five machine learning algorithms were trained to predict the skill level, and the support vector machine performed the best, with an accuracy of 92.41% and Area Under Curve value of0.98253. The machine learning model was interpreted using Shapley values to identify the important factors contributing to the skill level of each participant.</p></div><div><h3>Results</h3><p>This study demonstrates the effectiveness of machine learning in differentiating the evaluation and training of virtual reality neurosurgical per- formances. The use of Shapley values enables targeted training by identifying deficiencies in individual skills.</p></div><div><h3>Conclusions</h3><p>This study provides insights into the use of machine learning for personalized training in virtual reality neurosurgery. The interpretability of the machine learning models enables the development of individualized training programs. In addition, this study highlighted the potential of explanatory models in training external skills.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 1","pages":"Pages 17-29"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000451/pdf?md5=4a05396e17452331858ce0f3bf7464a8&pid=1-s2.0-S2096579623000451-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Minghua Jiang , Zhangyuan Tian , Chenyu Yu , Yankang Shi , Li Liu , Tao Peng , Xinrong Hu , Feng Yu
{"title":"Intelligent 3D garment system of the human body based on deep spiking neural network","authors":"Minghua Jiang , Zhangyuan Tian , Chenyu Yu , Yankang Shi , Li Liu , Tao Peng , Xinrong Hu , Feng Yu","doi":"10.1016/j.vrih.2023.07.002","DOIUrl":"https://doi.org/10.1016/j.vrih.2023.07.002","url":null,"abstract":"<div><h3>Background</h3><p>Intelligent garments, a burgeoning class of wearable devices, have extensive applications in domains such as sports training and medical rehabilitation. Nonetheless, existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity, often skipping crucial aspects related to user experience and interaction.</p></div><div><h3>Methods</h3><p>To address this gap, this study introduces a novel real-time 3D interactive system based on intelligent garments. The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements, thereby achieving real-time interaction between users and sensors. Additionally, the system in- corporates 3D human visualization functionality, which visualizes sensor data and recognizes human actions as 3D models in realtime, providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion. This system has significant potential for applications in motion detection, medical monitoring, virtual reality, and other fields. The accurate classification of human actions con- tributes to the development of personalized training plans and injury prevention strategies.</p></div><div><h3>Conclusions</h3><p>This study has substantial implications in the domains of intelligent garments, human motion monitoring, and digital twin visualization. The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 1","pages":"Pages 43-55"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S209657962300044X/pdf?md5=934866992a1e420fa2627cab1a89561d&pid=1-s2.0-S209657962300044X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Masanori Nakayama, Karin Uchino, Ken Nagao, Issei Fujishiro
{"title":"HYDRO: Optimizing Interactive Hybrid Images for Digital Signage Content","authors":"Masanori Nakayama, Karin Uchino, Ken Nagao, Issei Fujishiro","doi":"10.1016/j.vrih.2022.08.009","DOIUrl":"10.1016/j.vrih.2022.08.009","url":null,"abstract":"<div><p>In modern society, digital signage installed in many large-scale facilities supports our daily life. However, with a limited screen size, it is difficult to provide different types of information for many viewers at varying distances from the screen simultaneously. Therefore, in this study, we extend existing research on the use of hybrid images for tiled displays. To facilitate smoother information selection, a new interactive display method is proposed that incorporates a touchactivated widget as a high-frequency part of the hybrid image; these widgets are novel in that they are more visible to the viewers near to the display. We develop an authoring tool that we call the Hybrid Image Display Resolutions Optimizer (HYDRO); it features two kinds of control functions by which to optimize the visibility of the touch-activated widgets in terms of placement and resolution. The effectiveness of the present method is shown empirically via a quantitative user study and an eye-tracking-based qualitative evaluation.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"5 6","pages":"Pages 565-577"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579622000845/pdf?md5=66bf13c453add643fb720daf9ae46a21&pid=1-s2.0-S2096579622000845-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139021992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Learning Adequate Alignment and Interaction for Cross-Modal Retrieval","authors":"MingKang Wang , Min Meng , Jigang Liu , Jigang Wu","doi":"10.1016/j.vrih.2023.06.003","DOIUrl":"10.1016/j.vrih.2023.06.003","url":null,"abstract":"<div><p>Cross-modal retrieval has attracted widespread attention in many cross-media similarity search applications, especially image-text retrieval in the fields of computer vision and natural language processing. Recently, visual and semantic embedding (VSE) learning has shown promising improvements on image-text retrieval tasks. Most existing VSE models employ two unrelated encoders to extract features, then use complex methods to contextualize and aggregate those features into holistic embeddings. Despite recent advances, existing approaches still suffer from two limitations: 1) without considering intermediate interaction and adequate alignment between different modalities, these models cannot guarantee the discriminative ability of representations; 2) existing feature aggregators are susceptible to certain noisy regions, which may lead to unreasonable pooling coefficients and affect the quality of the final aggregated features. To address these challenges, we propose a novel cross-modal retrieval model containing a well-designed alignment module and a novel multimodal fusion encoder, which aims to learn adequate alignment and interaction on aggregated features for effectively bridging the modality gap. Experiments on Microsoft COCO and Flickr30k datasets demonstrates the superiority of our model over the state-of-the-art methods.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"5 6","pages":"Pages 509-522"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S209657962300027X/pdf?md5=12c947f69173683c04a27c84c4b305fc&pid=1-s2.0-S209657962300027X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139017416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ILIDViz: An Incremental Learning-Based Visual Analysis System for Network Anomaly Detection","authors":"Xuefei Tian, Zhiyuan Wu, JunXiang Cao, Shengtao Chen, Xiaoju Dong","doi":"10.1016/j.vrih.2023.06.009","DOIUrl":"10.1016/j.vrih.2023.06.009","url":null,"abstract":"<div><h3>Background</h3><p>With the development of information technology, network traffic logs mixed with various kinds of cyber-attacks have grown explosively. Traditional intrusion detection systems (IDS) have limited ability to discover new inconstant patterns and identify malicious traffic traces in real-time. It is urgent to implement more effective intrusion detection technologies to protect computer security.</p></div><div><h3>Methods</h3><p>In this paper, we design a hybrid IDS, combining our incremental learning model (KAN-SOINN) and active learning, to learn new log patterns and detect various network anomalies in real-time.</p></div><div><h3>Results & Conclusions</h3><p>The experimental results on the NSLKDD dataset show that the KAN-SOINN can be improved continuously and detect malicious logs more effectively. Meanwhile, the comparative experiments prove that using a hybrid query strategy in active learning can improve the model learning efficiency.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"5 6","pages":"Pages 471-489"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000372/pdf?md5=4b6332c477d34f662bbd8d1f6d5110ea&pid=1-s2.0-S2096579623000372-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139024181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junjie TAO , Yinghui WANG , Haomiao M.A , Tao YAN , Lingyu AI , Shaojie ZHANG , Wei LI
{"title":"An image defocus deblurring method based on gradient difference of boundary neighborhood","authors":"Junjie TAO , Yinghui WANG , Haomiao M.A , Tao YAN , Lingyu AI , Shaojie ZHANG , Wei LI","doi":"10.1016/j.vrih.2023.06.008","DOIUrl":"10.1016/j.vrih.2023.06.008","url":null,"abstract":"<div><h3>Background</h3><p>For static scenes with multiple depth layers, the existing defocused image deblurring methods have the problems of edge ringing artifacts or insufficient deblurring degree due to inaccurate estimation of blur amount, In addition, the prior knowledge in non blind deconvolution is not strong, which leads to image detail recovery challenge.</p></div><div><h3>Methods</h3><p>To this end, this paper proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood, which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring, thus preventing boundary ringing artifacts. Then, the obtained blur map is used for blur detection to determine whether the image needs to be deblurred, thereby improving the efficiency of deblurring without manual intervention and judgment. Finally, a non blind deconvolution algorithm is designed to achieve image deblurring based on the blur amount selection strategy and sparse prior.</p></div><div><h3>Results</h3><p>Experimental results show that our method improves PSNR and SSIM by an average of 4.6% and 7.3%, respectively, compared to existing methods.</p></div><div><h3>Conclusions</h3><p>Experimental results show that our method outperforms existing methods. Compared with existing methods, our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"5 6","pages":"Pages 538-549"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000402/pdf?md5=e25f0d04bda463effe6c2d3480b7f3ad&pid=1-s2.0-S2096579623000402-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139022286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"RealFuVSR: Feature Enhanced Real-World Video Super-Resolution","authors":"Zhi Li , Xiong Pang , Yiyue Jiang , Yujie Wang","doi":"10.1016/j.vrih.2023.06.006","DOIUrl":"10.1016/j.vrih.2023.06.006","url":null,"abstract":"<div><h3>Background</h3><p>The recurrent recovery is one of the common methods for video super-resolution, which models the correlation between frames via hidden states. However, when we apply the structure to real-world scenarios, it leads to unsatisfactory artifacts. We found that, in the real-world video super-resolution training, the use of unknown and complex degradation can better simulate the degradation process of the real world.</p></div><div><h3>Methods</h3><p>Based on this, we propose the RealFuVSR model, which simulates the real-world degradation and mitigates the artifacts caused by the video super-resolution. Specifically, we propose a multi-scale feature extraction module(MSF) which extracts and fuses features from multiple scales, it facilitates the elimination of hidden state artifacts. In order to improve the accuracy of hidden states alignment information, RealFuVSR use advanced optical flow-guided deformable convolution. Besides, cascaded residual upsampling module is used to eliminate the noise caused by the upsampling process.</p></div><div><h3>Results</h3><p>The experiment demonstrates that our RealFuVSR model can not only recover the high-quality video but also outperform the state-of-the-art RealBasicVSR and RealESRGAN models.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"5 6","pages":"Pages 523-537"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000396/pdf?md5=ccc376bebb752e8cce7b3633ad69bf64&pid=1-s2.0-S2096579623000396-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139012746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}