Displays最新文献

{"title":"Evaluating ASD in children through automatic analysis of paintings","authors":"Ji-Feng Luo ,&nbsp;Zhijuan Jin ,&nbsp;Xinding Xia ,&nbsp;Fangyu Shi ,&nbsp;Zhihao Wang ,&nbsp;Chi Zhang","doi":"10.1016/j.displa.2024.102850","DOIUrl":"10.1016/j.displa.2024.102850","url":null,"abstract":"<div><div>Autism spectrum disorder (ASD) is a hereditary neurodevelopmental disorder affecting individuals, families, and societies worldwide. Screening for ASD relies on specialized medical resources, and current machine learning-based screening methods depend on expensive professional devices and algorithms. Therefore, there is a critical need to develop accessible and easily implementable methods for ASD assessment. In this study, we are committed to finding such an ASD screening and rehabilitation assessment solution based on children’s paintings. From an ASD painting database, 375 paintings from children with ASD and 160 paintings from typically developing children were selected, and a series of image signal processing algorithms based on typical characteristics of children with ASD were designed to extract features from images. The effectiveness of extracted features was evaluated through statistical methods, and they were then classified using a support vector machine (SVM) and XGBoost (eXtreme Gradient Boosting). In 5-fold cross-validation, the SVM achieved a recall of 94.93%, a precision of 86.40%, an accuracy of 85.98%, and an AUC of 90.90%, while the XGBoost achieved a recall of 96.27%, a precision of 93.78%, an accuracy of 92.90%, and an AUC of 98.00%. This efficacy persists at a high level even during additional validation on a set of newly collected paintings. Not only did the performance surpass that of participated human experts, but the high recall rate, as well as its affordability, manageability, and ease of implementation, indicates potentiality in wide screening and rehabilitation assessment. All analysis code is public at GitHub: <span><span>dishangti/ASD-Painting-Pub</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102850"},"PeriodicalIF":3.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using query semantic and feature transfer fusion to enhance cardinality estimating of property graph queries","authors":"Zhenzhen He ,&nbsp;Tiquan Gu ,&nbsp;Jiong Yu","doi":"10.1016/j.displa.2024.102854","DOIUrl":"10.1016/j.displa.2024.102854","url":null,"abstract":"<div><div>With the increasing complexity and diversity of query tasks, cardinality estimation has become one of the most challenging problems in query optimization. In this study, we propose an efficient and accurate cardinality estimation method to address the cardinality estimation problem in property graph queries, particularly in response to the current research gap regarding the neglect of contextual semantic features. We first propose formal representations of the property graph query and define its cardinality estimation problem. Then, through the query featurization, we transform the query into a vector representation that can be learned by the estimation model, and enrich the feature vector representation by the context semantic information of the query. We finally propose an estimation model for property graph queries, specifically introducing a feature information transfer module to dynamically control the information flow meanwhile achieving the model’s feature fusion and inference. Experimental results on three datasets show that the estimation model can accurately and efficiently estimate the cardinality of property graph queries, the mean Q_error and RMSE are reduced by about 30% and 25% than the state-of-art estimation models. The context semantics features of queries can improve the model’s estimation accuracy, the mean Q_error result is reduced by about 20% and the RMSE result is about 5%.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102854"},"PeriodicalIF":3.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Profiles of cybersickness symptoms","authors":"Jonathan W. Kelly ,&nbsp;Nicole L. Hayes ,&nbsp;Taylor A. Doty ,&nbsp;Stephen B. Gilbert ,&nbsp;Michael C. Dorneich","doi":"10.1016/j.displa.2024.102853","DOIUrl":"10.1016/j.displa.2024.102853","url":null,"abstract":"<div><div>Cybersickness – discomfort caused by virtual reality (VR) – remains a significant problem that negatively affects the user experience. Research on individual differences in cybersickness has typically focused on overall sickness intensity, but a detailed understanding should include whether individuals differ in the relative intensity of cybersickness symptoms. This study used latent profile analysis (LPA) to explore whether there exist groups of individuals who experience common patterns of cybersickness symptoms. Participants played a VR game for up to 20 min. LPA indicated three groups with low, medium, and high overall cybersickness. Further, there were similarities and differences in relative patterns of nausea, disorientation, and oculomotor symptoms between groups. Disorientation was lower than nausea and oculomotor symptoms for all three groups. Nausea and oculomotor were experienced at similar levels within the high and low sickness groups, but the medium sickness group experienced more nausea than oculomotor. Characteristics of group members varied across groups, including gender, virtual reality experience, video game experience, and history of motion sickness. These findings identify distinct individual experiences in symptomology that go beyond overall sickness intensity, which could enable future interventions that target certain groups of individuals and specific symptoms.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102853"},"PeriodicalIF":3.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel heart rate estimation framework with self-correcting face detection for Neonatal Intensive Care Unit","authors":"Kangyang Cao,&nbsp;Tao Tan,&nbsp;Zhengxuan Chen,&nbsp;Kaiwen Yang,&nbsp;Yue Sun","doi":"10.1016/j.displa.2024.102852","DOIUrl":"10.1016/j.displa.2024.102852","url":null,"abstract":"<div><div>Remote photoplethysmography (rPPG) is a non-invasive method for monitoring heart rate (HR) and other vital signs by measuring subtle facial color changes caused by blood flow variations beneath the skin, typically captured through video-based imaging. Current rPPG technology, which is optimized for ideal conditions, faces significant challenges in real-world clinical settings such as Neonatal Intensive Care Units (NICUs). These challenges primarily arise from the limitations of automatic face detection algorithms embedded in HR estimation frameworks, which have difficulty accurately detecting the faces of newborns. Additionally, variations in lighting conditions can significantly affect the accuracy of HR estimation. The combination of these positional changes and fluctuations in lighting significantly impacts the accuracy of HR estimation. To address the challenges of inadequate face detection and HR estimation in newborns, we propose a novel HR estimation framework that incorporates a Self-Correcting face detection module. Our HR estimation framework introduces an innovative rPPG value reference module to mitigate the effects of lighting variations, significantly reducing HR estimation error. The Self-Correcting module improves face detection accuracy by enhancing robustness to occlusions and position changes while automating the process to minimize manual intervention. Our proposed framework demonstrates notable improvements in both face detection accuracy and HR estimation, outperforming existing methods for newborns in NICUs.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102852"},"PeriodicalIF":3.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Salient Object Ranking: Saliency model on relativity learning and evaluation metric on triple accuracy","authors":"Yingchun Guo,&nbsp;Shu Chen,&nbsp;Gang Yan,&nbsp;Shi Di,&nbsp;Xueqi Lv","doi":"10.1016/j.displa.2024.102855","DOIUrl":"10.1016/j.displa.2024.102855","url":null,"abstract":"<div><div>Salient object ranking (SOR) aims to evaluate the saliency level of each object in an image, which is crucial for the advancement of downstream tasks. The human visual system distinguishes the saliency levels of different targets in a scene by comprehensively utilizing multiple saliency cues. To mimic this comprehensive evaluation behavior, the SOR task needs to consider both the objects’ intrinsic information and their relative information within the entire image. However, existing methods still struggle to obtain relative information effectively, which tend to focus too much on specific objects while ignoring their relativity. To address these issues, this paper proposes a Salient Object Ranking method based on Relativity Learning (RLSOR), which integrates multiple saliency cues to learn the relative information among objects. RLSOR consists of three main modules: the Top-down Guided Salience Regulation module (TGSR), the Global–Local Cooperative Perception module (GLCP), and the Semantic-guided Edge Enhancement module (SEE). At the same time, this paper proposes a Triple-Accuracy Evaluation (TAE) metric for the SOR task, which can evaluate the segmentation accuracy, relative ranking accuracy, and absolute ranking accuracy in one metric. Experimental results show that RLSOR significantly enhances SOR performance, and the proposed SOR evaluation metric can better meets human subjective perceptions.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102855"},"PeriodicalIF":3.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DZ-SLAM: A SAM-based SLAM algorithm oriented to dynamic environments","authors":"Zhe Chen ,&nbsp;Qiuyu Zang ,&nbsp;Kehua Zhang","doi":"10.1016/j.displa.2024.102846","DOIUrl":"10.1016/j.displa.2024.102846","url":null,"abstract":"<div><div>Precise localization is a fundamental prerequisite for the effective operation of Simultaneous Localization and Mapping (SLAM) systems. Traditional visual SLAM is based on static environments and therefore performs poorly in dynamic environments. While numerous visual SLAM methods have been proposed to address dynamic environments, these approaches are typically based on certain prior knowledge. This paper introduces DZ-SLAM, a dynamic SLAM algorithm that does not require any prior knowledge, based on ORB-SLAM3, to handle unknown dynamic elements in the scene. This work first introduces the FastSAM to enable comprehensive image segmentation. It then proposes an adaptive threshold-based dense optical flow approach to identify dynamic elements within the environment. Finally, combining FastSAM with optical flow method and embedding it into the SLAM framework to eliminate dynamic objects and improve positioning accuracy in dynamic environments. The experiment shows that compared with the original ORB-SLAM3 algorithm, the algorithm proposed in this paper reduces the absolute trajectory error by up to 96%; Compared to the most advanced algorithms currently available, the absolute trajectory error of our algorithm can be reduced by up to 46%. In summary, the proposed dynamic object segmentation method without prior knowledge can significantly reduce the positioning error of SLAM algorithm in various dynamic environments.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102846"},"PeriodicalIF":3.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pen-based vibrotactile feedback rendering of surface textures under unconstrained acquisition conditions","authors":"Miao Zhang ,&nbsp;Dongyan Nie ,&nbsp;Weizhi Nai ,&nbsp;Xiaoying Sun","doi":"10.1016/j.displa.2024.102844","DOIUrl":"10.1016/j.displa.2024.102844","url":null,"abstract":"<div><div>Haptic rendering of surface textures enhances user immersion of human–computer interaction. However, strict input conditions and measurement methods limit the diversity of rendering algorithms. In this regard, we propose a neural network-based approach for vibrotactile haptic rendering of surface textures under unconstrained acquisition conditions. The method first encodes the interactions based on human perception characteristics, and then utilizes an autoregressive-based model to learn a non-linear mapping between the encoded data and haptic features. The interactions consist of normal forces and sliding velocities, while the haptic features are time–frequency amplitude spectrograms by short-time Fourier transform of the accelerations corresponding to the interactions. Finally, a generative adversarial network is employed to convert the generated time–frequency amplitude spectrograms into the accelerations. The effectiveness of the proposed approach is confirmed through numerical calculations and subjective experiences. This approach enables the rendering of a wide range of vibrotactile data for surface textures under unconstrained acquisition conditions, achieving a high level of haptic realism.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102844"},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of machine learning methods for display characterization","authors":"Khleef Almutairi ,&nbsp;Samuel Morillas ,&nbsp;Pedro Latorre-Carmona ,&nbsp;Makan Dansoko ,&nbsp;María José Gacto","doi":"10.1016/j.displa.2024.102849","DOIUrl":"10.1016/j.displa.2024.102849","url":null,"abstract":"<div><div>This paper explores the application of various machine-learning methods for characterizing displays of technologies LCD, OLED, and QLED to achieve accurate color reproduction. These models are formed from input (device-dependent RGB data) and output (device-independent XYZ coordinates) data obtained from three different displays. Training and test datasets are built using <span><math><mrow><mi>R</mi><mi>G</mi><mi>B</mi></mrow></math></span> data measured directly from the displays and corresponding <span><math><mrow><mi>X</mi><mi>Y</mi><mi>Z</mi></mrow></math></span> coordinates measured with a high-precision colorimeter. A key aspect of this research is the application fuzzy inference systems for building interpretable models. These models offer the advantage of not only achieving good performance in color reproduction, but also providing physical insights into the relationships between the <span><math><mrow><mi>R</mi><mi>G</mi><mi>B</mi></mrow></math></span> inputs and the resulting <span><math><mrow><mi>X</mi><mi>Y</mi><mi>Z</mi></mrow></math></span> outputs. This interpretability allows for a deeper understanding of the display’s behavior. Furthermore, we compare the performance of fuzzy models with other popular machine-learning approaches, including those based on neural networks and decision trees. By evaluating the strengths and weaknesses of each method, we aim to identify the most effective approach for display characterization. The effectiveness of each method is assessed by its ability to accurately reproduce and display colors, as measured by the <span><math><mrow><mi>Δ</mi><msub><mrow><mi>E</mi></mrow><mrow><mn>00</mn></mrow></msub></mrow></math></span> visual error metric. Our findings indicate that the Fuzzy Modeling and Identification (FMID) method is particularly effective, allowing for an optimal balance between high accuracy and interpretability. Its competitive performance across all display types, combined with its valuable interpretability, provides insights for potential future calibration and optimization strategies. The results will shed light on whether machine learning methods offer an advantage over traditional physical models, particularly in scenarios with limited data. Additionally, the study will contribute to the understanding of the interpretability benefits offered by fuzzy inference systems in the context of LCD display characterization.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102849"},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TRRHA: A two-stream re-parameterized refocusing hybrid attention network for synthesized view quality enhancement","authors":"Ziyi Cao ,&nbsp;Tiansong Li ,&nbsp;Guofen Wang ,&nbsp;Haibing Yin ,&nbsp;Hongkui Wang ,&nbsp;Li Yu","doi":"10.1016/j.displa.2024.102843","DOIUrl":"10.1016/j.displa.2024.102843","url":null,"abstract":"<div><div>In multi-view video systems, the decoded texture video and its corresponding depth video are utilized to synthesize virtual views from different perspectives using the depth-image-based rendering (DIBR) technology in 3D-high efficiency video coding (3D-HEVC). However, the distortion of the compressed multi-view video and the disocclusion problem in DIBR can easily cause obvious holes and cracks in the synthesized views, degrading the visual quality of the synthesized views. To address this problem, a novel two-stream re-parameterized refocusing hybrid attention (TRRHA) network is proposed to significantly improve the quality of synthesized views. Firstly, a global multi-scale residual information stream is applied to extract the global context information by using refocusing attention module (RAM), and the RAM can detect the contextual feature and adaptively learn channel and spatial attention feature to selectively focus on different areas. Secondly, a local feature pyramid attention information stream is used to fully capture complex local texture details by using re-parameterized refocusing attention module (RRAM). The RRAM can effectively capture multi-scale texture details with different receptive fields, and adaptively adjust channel and spatial weights to adapt to information transformation at different sizes and levels. Finally, an efficient feature fusion module is proposed to effectively fuse the extracted global and local information streams. Extensive experimental results show that the proposed TRRHA achieves significantly better performance than the state-of-the-art methods. The source code will be available at <span><span>https://github.com/647-bei/TRRHA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102843"},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasickness and partial peripheral vision obstruction with versus without an artificial horizon","authors":"Camille de Thierry de Faletans,&nbsp;Maxime Misericordia,&nbsp;Jean-Marc Vallier,&nbsp;Pascale Duché,&nbsp;Eric Watelain","doi":"10.1016/j.displa.2024.102851","DOIUrl":"10.1016/j.displa.2024.102851","url":null,"abstract":"<div><div>Motion sickness (MS) is common when subjects are exposed to unfamiliar motion and affect individuals during travel. This study examines the immediate effect of two visual devices, in the form of glasses, on MS symptoms and associated physiological effects. The hypothesis is that peripheral vision obstruction reduces MS and that an additional beneficial effect could be observed when it is combined with an artificial horizon. Fifteen subjects with moderate to severe susceptibility to MS were exposed to a boat simulator in three conditions. Symptoms were assessed immediately after exposure. Time spent in the simulator, heart rate, and temperature were also recorded. The intensity of symptoms at the end of the experience did not differ, but time spent in the simulator before the onset of symptoms was significantly longer with peripheral vision obstruction (+36 %) and with both techniques combined (+40 %) than in the control condition. No difference was observed between the combined condition and peripheral vision obstruction alone. The glasses device used in this study (with or without an artificial horizon) delays the onset of symptoms. Further research is needed to confirm the mechanism that explains the benefits and to evaluate these effects during prolonged exposure to MS-inducing stimuli or after a period of familiarization with the device.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"85 ","pages":"Article 102851"},"PeriodicalIF":3.7,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}