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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Predicting the Risk of Incident Cardiovascular Disease Using Retinal Vessel Caliber Measured by Artificial Intelligence in a Chinese Community-Based Population

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SSRN 2025年

作者： Luo, Xingyu Yang, Guangzhengao Fan, Fangfang Jiang, Yimeng Jia, Jia Zhao, Jie Chen, Zifan Yan, Shenshen She, Haicheng Zheng, Bo Zhang, Li Zhang, Yan Department of Cardiology Peking University First Hospital Beijing China Institute of Cardiovascular Disease Peking University First Hospital Beijing China Center for Data Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Changsha Institute for Computing and Digital Economy Beijing China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Science Key Lab Beijing China

Background: The predictive value of retinal vessel caliber, measured through artificial intelligence (AI), for incident cardiovascular disease (CVD) risk remains to be explored. Our study aimed to investigate the association between AI-measured retinal vessel caliber and the risk of incident CVD in a Chinese community-based population. Methods: We conducted a prospective cohort study on subjects from a Beijing community in China who were free of prevalent stroke or myocardial infarction and underwent retinal photography at baseline. By deep-learning algorithm, retinal vessel caliber measurements, including central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), and arteriolar-to-venular ratio (AVR), were obtained from 0.5 to 1 disc diameter from the optic disc margin. The primary endpoint was an incident major adverse cardiovascular event (MACE), defined as a composite of first non-fatal stroke, first non-fatal acute myocardial infarction (AMI), and cardiovascular death. The secondary endpoints included first stroke, first AMI, and cardiovascular death. Findings: A total of 3,585 participants were included. The mean age was 56.49 ± 8.94 years, and 2335 (65.1%) were female. Prevalence of prior hypertension and diabetes was 1594 (44.5%) and 676 (18.9%), respectively. The mean values for CRAE, CRVE, and AVR were 190.83 ± 29.92 μm, 278.26 ± 36.34 μm, and 0.69 ± 0.08, respectively. During the follow-up (median, 7.53 years), there were 293 cases of incident MACE (8.2%), 226 strokes (6.3%), 57 AMIs (1.6%), and 44 cardiovascular deaths (1.2%). The Kaplan–Meier curves showed significant differences in cumulative hazards of the primary endpoint among tertiles of CRAE, CRVE, and AVR. In Cox proportional-hazards models adjusting for covariates, CRAE was negatively associated with the risk of incident MACE (per 10 μm increase, hazard ratio [HR] = 0.95, 95% confidence interval [CI]: 0.92-0.99, P = 0.009) and the risk of incident stroke (per 10 μm

关键词： Risk assessment

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AEP: An adaptive ensemble P300-BCI classifier based on user-feedback and knowledge-transfer

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Applied Intelligence 2025年第8期55卷 1-13页

作者： Huang, Zhihua Chen, Qingzhi Chen, Xuewei Zheng, Wenming Lin, Zhixiong Luo, Tian-Jian College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Higher Education Applied Technology Center for Visual Perception and Intelligent Analysis Quanzhou Vocational College of Economics and Business Quanzhou362000 China Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing210096 China Department of Neurosurgery Sanbo Brain Hospital of Capital Medical University Beijing100093 China College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

As a stable and reliable paradigm, P300-based brain-computer interface (P300-BCI) is expected to play an important role in efforts to replace, restore, enhance, supplement, or improve the natural output of the brain. However, the costly calibration of P300-BCI limits its development. The calibration-free approaches for P300-BCI have become a research focus in the field. In this work, we forwarded our previous study, transferred P300 linear upper confidence bound (TPLUCB), to propose an adaptive ensemble P300-BCI classifier (AEP). This renovation mainly includes a simplified calculation method and a dynamical update strategy. The competitive calculation model in TPLUCB was simplified as a linear calculation model. Based on this, a dynamical update strategy was proposed to facilitate the growth of target domain model and optimize the weights, by which the source domain models and the target domain model are combined as a P300-BCI classifier, i.e. AEP. We conducted the performance evaluation by observing the classifier’s dynamical development and overall performance. The comparison in the two aspects between AEP and TPLUCB demonstrates AEP’s clear advantage over TPLUCB. Without prior calibration, AEP achieved an average ITR exceeding 40 bit/min on electroencephalogram (EEG) data of 20 subjects. This work has provided a better calibration-free approach for P300-BCI and is an important step towards promoting the research on calibration-free BCIs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Reinforcement learning

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Exploring Uncertainty in Image Segmentation Ensembles

Exploring Uncertainty in Image Segmentation Ensembles

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20th Eurographics/IEEE VGTC Conference on visualization, EuroVis 2018

作者： Fröhler, B. Möller, T. Weissenböck, J. Hege, H.-C. Kastner, J. Heinzl, C. Research Group Computed Tomography University of Applied Sciences Upper Austria Austria Visualization and Data Analysis Research Group Faculty of Computer Science University of Vienna Austria Visual Data Analysis Department Zuse Institute Berlin Germany

ISBN: (纸本)9783038680659

Finding the most accurate image segmentation involves analyzing results from different algorithms or parameterizations. In this work, we identify different types of uncertainty in this analysis that are represented by the results of probabilistic algorithms, by the local variability in the segmentation, and by the variability across the segmentation ensemble. We propose visualization techniques for the analysis of such types of uncertainties in segmentation ensembles. For a global analysis we provide overview visualizations in the image domain as well as in the label space. Our probability probing and scatter plot based techniques facilitate a local analysis. We evaluate our techniques using a case study on industrial computed tomography data. © 2018 The Author(s)

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A High-Dimensional data Quality Metric using Pareto Optimality

A High-Dimensional Data Quality Metric using Pareto Optimali...

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19th Eurographics / IEEE VGTC Conference on visualization, EuroVis 2017

作者： Post, Tobias Wischgoll, Thomas Hamann, Bernd Hagen, Hans Computer Graphics and HCI Group University of Kaiserslautern Germany Advanced Visual Data Analysis Group Wright State University United States Department of Computer Science University of California Davis United States

ISBN: (纸本)9783038680444

The representation of data quality within established high-dimensional data visualization techniques such as scatterplots and parallel coordinates is still an open problem. This work offers a scale-invariant measure based on Pareto optimality that is able to indicate the quality of data points with respect to the Pareto front. In cases where datasets contain noise or parameters that cannot easily be expressed or evaluated mathematically, the presented measure provides a visual encoding of the environment of a Pareto front to enable an enhanced visual inspection. © 2017 The Author(s) Eurographics Proceedings © 2017 The Eurographics Association.

关键词： data visualization

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Intuitive Error Space Exploration of Medical Image data in Clinical Daily Routine

Intuitive Error Space Exploration of Medical Image Data in C...

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19th Eurographics/IEEE VGTC Conference on visualization, EuroVis 2017

作者： Gillmann, Christina Arbeláez, Pablo Hernández, José Tiberio Hagen, Hans Wischgoll, Thomas Computergraphics and HCI University of Kaiserslautern Germany Department of Biomedical Engineering University of los Andes Colombia IMAGINE Group University of los Andes Colombia Advanced Visual Data Analysis Wright State University United States

ISBN: (纸本)9783038680437

Medical image data can be affected by several image errors. These errors can lead to uncertain or wrong diagnosis in clinical daily routine. A large variety of image error metrics are available that target different aspects of image quality forming a high-dimensional error space, which cannot be reviewed trivially. To solve this problem, this paper presents a novel error space exploration technique that is suitable for clinical daily routine. Therefore, the clinical workflow for reviewing medical data is extended by error space cluster information, that can be explored by user-defined selections. The presented tool was applied to two real-world datasets to show its effectiveness. © 2017 The Author(s) Eurographics Proceedings © 2017 The Eurographics Association.

关键词： visualization

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PraK Tool: An Interactive Search Tool Based on Video data Services 30th

PraK Tool: An Interactive Search Tool Based on Video Data S...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Lokoč, Jakub Vopálková, Zuzana Stroh, Michael Buchmueller, Raphael Schlegel, Udo SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9783031533013

This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent development of interactive search interfaces and refinement techniques, which is demonstrated by a smart front-end component. The component supports common search features and allows users to exploit content-based statistics for effective filtering. We believe that video data services might be a valuable addition to the open-source VBS toolkit, especially when available for the competition on a shared server with all VBS datasets, extracted features, and meta-data behind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Video recording

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visual perception alterations in COVID-19:a preliminary study

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International Journal of Ophthalmology(English edition) 2023年第1期16卷 1-9页

作者： María Begoña Coco-Martín Luis Leal-Vega Irene Alcoceba-Herrero Ainhoa Molina-Martín Dolores de-Fez María JoséLuque Carlos Dueñas-Gutiérrez Juan Francisco Arenillas-Lara David PPiñero Group of Applied Clinical Neurosciences and Advanced Data Analysis Department of MedicineDermatology and ToxicologyUniversity of ValladolidValladolid 47005Spain Group of Optics and Visual Perception Department of OpticsPharmacology and AnatomyUniversity of AlicanteAlicante 03690Spain Department of Optics and Optometry and Vision SciencesUniversity of ValenciaValencia 46100Spain COVID-19 Unit Department of Internal MedicineUniversity Clinical Hospital of ValladolidValladolid 47003Spain Stroke Unit Department of NeurologyUniversity Clinical Hospital of ValladolidValladolid 47003Spain Clinical Optometry Unit Department of OphthalmologyVithas Medimar International HospitalAlicante 03016Spain

AIM:To compare the visual perception(color and chromatic-achromatic contrast vision)of a small cohort of COVID-19 patients at the time of infection and after 6mo with that of a healthy population matched for sex and ***:A total of 25 patients(9 females,16 males,mean age:54±10y)with COVID-19 hospitalized in the COVID-19 Unit of the University Clinical Hospital of Valladolid were recruited for this preliminary *** perception,as determined by monocular measurement of contrast sensitivity function(CSF)and color vision was assessed in each patient using the Optopad *** results obtained were then compared with those of a sample of 16 age-and sex-matched healthy controls(5 females,11 males,mean age:50±6y)in which the same measurement procedure was *** significant differences between groups were assessed using the Mann-Whitney U *** were repeated after a minimum follow-up period of 6mo and statistically significant differences between the two time points in each group were assessed using the Wilcoxon signed rank ***:Discrimination thresholds(color and chromatic-achromatic contrast vision)and their corresponding sensitivity,calculated as the inverse of the discrimination threshold,were *** of the data revealed higher contrast threshold results(i.e.,worse contrast sensitivity)in the COVID-19 group than in the control group for all spatial frequencies studied in the Optopad-CSF achromatic test and most of the spatial frequencies studied in the Optopad-CSF chromatic test for the red-green and blue-yellow *** addition,color threshold results in the COVID-19 group were also significantly higher(i.e.,worse color sensitivity)for almost all color mechanisms studied in the Optopad-Color *** 6mo,most of the differences found between the groups were maintained despite COVID-19 ***:The present results provide preliminary evidence that visual perception may be impaired in COVID-19

关键词： SARS-CoV-2 COVID-19 visual perception contrast sensitivity function color vision

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Boosting segmentation accuracy of the deep learning models based on the synthetic data generation 4

Boosting segmentation accuracy of the deep learning models b...

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4th International Workshop on Photogrammetric and Computer Vision Techniques for Video Surveillance, Biometrics and Biomedicine, PSBB 2021

作者： Danilov, V.V. Gerget, O.M. Kolpashchikov, D.Yu. Laptev, N.V. Manakov, R.A. Hérnandez-Gómez, L.A. Alvarez, F. Ledesma-Carbayo, M.J. Research Laboratory for Processing and Analysis of Big Data Tomsk Polytechnic University Tomsk Russia Department of Software Engineering Research Laboratory"Gamma Technologies" Almaty Kazakhstan Department of Signals Systems and Radiocommunications Technical University of Madrid Madrid Spain Group of Application of Visual Telecommunications Technical University of Madrid Madrid Spain Biomedical Image Technologies Group Technical University of Madrid Madrid Spain

In the era of data-driven machine learning algorithms, data represents a new oil. The application of machine learning algorithms shows they need large heterogeneous datasets that crucially are correctly labeled. However, data collection and its labeling are time-consuming and labor-intensive processes. A particular task we solve using machine learning is related to the segmentation of medical devices in echocardiographic images during minimally invasive surgery. However, the lack of data motivated us to develop an algorithm generating synthetic samples based on real datasets. The concept of this algorithm is to place a medical device (catheter) in an empty cavity of an anatomical structure, for example, in a heart chamber, and then transform it. To create random transformations of the catheter, the algorithm uses a coordinate system that uniquely identifies each point regardless of the bend and the shape of the object. It is proposed to take a cylindrical coordinate system as a basis, modifying it by replacing the Z-axis with a spline along which the h-coordinate is measured. Having used the proposed algorithm, we generated new images with the catheter inserted into different heart cavities while varying its location and shape. Afterward, we compared the results of deep neural networks trained on the datasets comprised of real and synthetic data. The network trained on both real and synthetic datasets performed more accurate segmentation than the model trained only on real data. For instance, modified U-net trained on combined datasets performed segmentation with the Dice similarity coefficient of 92.6±2.2%, while the same model trained only on real samples achieved the level of 86.5±3.6%. Using a synthetic dataset allowed decreasing the accuracy spread and improving the generalization of the model. It is worth noting that the proposed algorithm allows reducing subjectivity, minimizing the labeling routine, increasing the number of samples, and improving the heterog

关键词： Echocardiography

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visualization of Biomolecular Structures: State of the Art 17

Visualization of Biomolecular Structures: State of the Art

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17th Eurographics Conference on visualization - State of the Art Reports, EuroVis-STAR 2015

作者： Kozlíková, B. Krone, M. Lindow, N. Falk, M. Baaden, M. Baum, D. Viola, I. Parulek, J. Hege, H.-C. Faculty of Informatics Masaryk University Czech Republic Visualization Research Center University of Stuttgart Germany Department of Visual Data Analysis Zuse Institute Berlin Germany Immersive Visualization Group Linköping University Sweden Laboratoire de Biochimie Théorique UPR 9080 CNRS France Department of Informatics University of Bergen Norway Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

ISBN: (纸本)9783905674996

Structural properties of molecules are of primary concern in many fields. This report provides a comprehensive overview on techniques that have been developed in the fields of molecular graphics and visualization with a focus on applications in structural biology. The field heavily relies on computerized geometric and visual representations of three-dimensional, complex, large, and time-varying molecular structures. The report presents a taxonomy that demonstrates which areas of molecular visualization have already been extensively investigated and where the field is currently heading. It discusses visualizations for molecular structures, strategies for efficient display regarding image quality and frame rate, covers different aspects of level of detail, and reviews visualizations illustrating the dynamic aspects of molecular simulation data. The report concludes with an outlook on promising and important research topics to enable further success in advancing the knowledge about interaction of molecular structures. © The Eurographics Association 2015.

关键词： visualization

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