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IEEE Workshop on Applications of computer Vision (WACV)

作者： Lukas Mehl Andrés Bruhn Markus Gross Christopher Schroers Institute for Visualization and Interactive Systems University of Stuttgart Department of Computer Science Computer Graphics Lab ETH Zurich DisneyResearch|Studios Switzerland

Despite of exciting advances in image-based rendering and novel view synthesis, it is still challenging to achieve high-resolution results that can reach production-level quality when applying such methods to the task of stereo conversion. At the same time, only very few dedicated stereo conversion approaches exist, which also fall short in terms of the required quality. Hence, in this paper, we present a novel method for high-resolution 2D-to-3D conversion. It is fully differentiable in all of its stages and performs disparity-informed warping, consistent foreground-background compositing, and background-aware inpainting. To enable temporal consistency in the resulting video, we propose a strategy to integrate information from additional video frames. Extensive ablation studies validate our design choices, leading to a fully automatic model that outperforms existing approaches by a large margin (49-70% LPIPS error reduction). Finally, inspired from current practices in manual stereo conversion, we introduce optional interactive tools into our model, which allow to steer the conversion process and make it significantly more applicable for 3D film production.

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Evaluation of importance for condition attributes based on quality of decision reducts 26

Evaluation of importance for condition attributes based on q...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Relative or decision reducts belong with mechanisms dedicated to feature selection, and they are embedded in rough set approach to data processing. Algorithms for reduct construction typically aim at dimensionality reduction aspect, searching for smallest reducts, which are considered as the most advantageous from the point of view of knowledge representation. However, classifiers build on reduced data models, based on reducts, can significantly vary in performance. Therefore, to ensure quality of predictions, other characteristics of reducts, apart from their cardinalities, need to be taken into account. The paper presents research in which estimation of reduct quality through their characteristics was reflected in calculation of the proposed weighting factors leading to attribute rankings. These rankings were next employed in the process of filtering decision rules, inferred by classic rough set approach. Constructed rule-based classifiers were applied in the stylometric domain to solve a task of authorship attribution. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

关键词： Rough set theory

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Gamified Virtual Reality Training for Visuospatial Ability in Neuroradiology

Gamified Virtual Reality Training for Visuospatial Ability i...

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作者： Allgaier, Mareen Jered Härtel, Tim Zubel, Seraphine Thormann, Maximilian Behme, Daniel Preim, Bernhard Saalfeld, Sylvia Department of Simulation and Graphics Otto-von-Guericke-University Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Neuroradiology University Hospital Magdeburg Magdeburg39120 Germany Department of Computer Science and Automation Ilmenau University of Technology Ilmenau98693 Germany

During neuroradiological interventions such as treating intracranial aneurysms, neuroradiologists have to navigate a catheter in the 3D anatomy while watching a 2D projection of the vessels. To train this visuospatial ability, we present a virtual reality application where the user has to identify anatomically relevant positions on the Circle of Willis based on a 2D projection image. This image is generated based on a 3D model using a Fresnel shader. To increase motivation, which is an important aspect of training, we included several game elements. We qualitatively evaluated our application with two neuroradiologists. Although they rated our implementation of the task as difficult due to the small image section, both participants emphasized the importance of this training. Even small changes that have been suggested can adjust the difficulty, leading to an appropriate training that could be integrated into the curriculum. The results also indicate that the included game elements are motivating and that the application in general has a high usability. The proposed immersive virtual reality application provides training for visuospatial ability in a concrete medical context. Due to gamification, fun, and motivation are increased, which are two important factors of training affecting the frequency of use and outcome. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Virtual environments

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On heterogeneity or sub-classes aspect in construction of stylometric input datasets 26

On heterogeneity or sub-classes aspect in construction of st...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Stanczyk, Urszula Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Stylometric analysis of texts relies on learning characteristic traits of writing styles for authors. Once these patterns are discovered, they can be compared to the ones present in other text samples, to recognise their authorship. This recognition can be compromised if input datasets are prepared without taking into consideration possible stratification of the input space, leading to specific grouping of datapoints, or sub-classes within distinguished classes. The paper shows research dedicated to construction of various structures of input datasets, and combinations of such structures between train and test sets. In the research the influence of different stratification forms on the performance of selected popular classification systems was observed. To minimise the number of influencing factors, a task of authorship attribution was performed as binary classification with balanced classes. Stylometric descriptors exploited belonged to lexical and syntactic group, giving frequencies of occurrence for chosen style-markers. It resulted in real-valued attributes and these values were explored without applying discretisation, in order to avoid the possible bias of this procedure on observations. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

关键词： Knowledge based systems

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A Data-Driven Platform for the Coordination of Independent Visual Analytics Tools

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computers and graphics (Pergamon) 2022年 106卷 152-160页

作者： Nonnemann, Lars Hogräfer, Marius Röhlig, Martin Schumann, Heidrun Urban, Bodo Schulz, Hans-Jörg University of Rostock Institute for Visual and Analytic Computing Albert-Einstein-Strasse 22 Rostock18059 Germany University of Aarhus Department of Computer Science Abogade 34 Aarhus8200 Denmark Fraunhofer Institute for Computer Graphics Research Joachim-Jungius-Strasse 11 Rostock18059 Germany

Visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual analytics tool needs to support their required API or architecture. Contrary to most existing frameworks, we present an approach that does not rely on a single predefined interchange mechanism for the entire ensemble of VA tools. Instead, we propose using any available channel for data exchange between two consecutive VA tools. This allows mixing and matching of different data exchange strategies over the course of a cross-tool analysis. In this paper, we identify the challenges associated with establishing such tool chaining platform for data-driven coordination. We further describe the structure and capabilities of data exchange and explain various functionalities of our platform in detail. Based on a demonstrating example, we discuss the limitations of our approach and elaborate new insight for the coordination of the visual output of multiple VA tools. © 2022 Elsevier Ltd

关键词： Electronic data interchange

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Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

Fuzzy Querying in the Cloud-based Environment for Data Strea...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Romanow, Dominik Grzesik, Piotr Benecki, Pawel Niyomugaba, Alexandre Habimana, Theodore Kostrzewa, Daniel Tokarz, Krzysztof Hung, Che-Lun Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Ines Ruhengeri - Institute of Applied Sciences Department of Computer Science Rwanda Silesian University of Technology Department of Applied Informatics Gliwice Poland Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei Taiwan

ISBN: (纸本)9798350362480

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models. © 2024 IEEE.

关键词： Data streams

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AI-based character generation for disease stories: a case study using epidemiological data to highlight preventable risk factors

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i-com 2025年第1期24卷 259-281页

作者： Mittenentzwei, Sarah Garrison, Laura A. Budich, Beatrice Lawonn, Kai Dockhorn, Alexander Preim, Bernhard Meuschke, Monique Department of Simulation and Graphics Otto-von-Guericke University of Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Informatics University of Bergen Thormøhlens Gate 55 Bergen5020 Norway Faculty of Mathematics and Computer Science University of Jena Ernst-Abbe-Platz 2 Jena07743 Germany Institute of Information Processing Leibniz University Hannover Welfengarten 1 Hannover30167 Germany

Data-driven storytelling has grown significantly, becoming prevalent in various fields, including healthcare. In medical narratives, characters are crucial for engaging audiences, making complex medical information accessible, and potentially influencing positive behavioral and lifestyle changes. However, designing characters that are both educational and relatable to effectively engage audiences is challenging. We propose a GenAI-assisted pipeline for character design in data-driven medical stories, utilizing Stable Diffusion, a deep learning text-to-image model, to transform data into visual character representations. This approach reduces the time and artistic skills required to create characters that reflect the underlying data. As a proof-of-concept, we generated and evaluated two characters in a crowd-sourced case study, assessing their authenticity to the underlying data and consistency over time. In a qualitative evaluation with four experts with knowledge in design and health communication, the characters were discussed regarding their quality and refinement opportunities. The characters effectively conveyed various aspects of the data, such as emotions, age, and body weight. However, generating multiple consistent images of the same character proved to be a significant challenge. This underscores a key issue in using generative AI for character creation: the limited control designers have over the output. © 2025 the author(s), published by De Gruyter, Berlin/Boston.

关键词： data-storytelling GenAI medicine healthcare character generation

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Spiking Convolutional Vision Transformer

Spiking Convolutional Vision Transformer

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International Conference on Semantic Computing

作者： Sameerah Talafha Banafsheh Rekabdar Christos Mousas Chinwe Ekenna Department of Computer Science Southren Illinois University IL USA Department of Computer Science Portland State University OR USA Department of Computer Graphics Technology Purdue University IN USA Department of Computer Science University at Albany NY USA

This paper introduces the design of a Spiking Convolutional Vision Transformer (SCvT) by using the biological-inspired learning rules including spike-timing-dependent plasticity (STDP) and reward–modulated STDP (R-STDP) for a classification task. We describe the structure of the SCvT simulated with deep spiking convolutional neural networks (DSCNN) and give a detailed description of the classification strategy.

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AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition

arXiv

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arXiv 2024年

作者： Boutros, Fadi Štruc, Vitomir Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany University of Ljubljana Ljubljana Slovenia Department of Computer Science TU Darmstadt Darmstadt Germany

Knowledge distillation (KD) aims at improving the performance of a compact student model by distilling the knowledge from a high-performing teacher model. In this paper, we present an adaptive KD approach, namely AdaDistill, for deep face recognition. The proposed AdaDistill embeds the KD concept into the softmax loss by training the student using a margin penalty softmax loss with distilled class centers from the teacher. Being aware of the relatively low capacity of the compact student model, we propose to distill less complex knowledge at an early stage of training and more complex one at a later stage of training. This relative adjustment of the distilled knowledge is controlled by the progression of the learning capability of the student over the training iterations without the need to tune any hyper-parameters. Extensive experiments and ablation studies show that AdaDistill can enhance the discriminative learning capability of the student and demonstrate superiority over various state-of-the-art competitors on several challenging benchmarks, such as IJB-B, IJB-C, and ICCV2021-MFR. © 2024, CC BY-NC-SA.

关键词： Distillation

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Context-based Surface Pattern Completion of Ancient Pottery

Context-based Surface Pattern Completion of Ancient Pottery

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20th Eurographics Workshop on graphics and Cultural Heritage, GCH 2022

作者： Lengauer, Stefan Preiner, Reinhold Sipiran, Ivan Karl, Stephan Tinkl, Elisabeth Bustos, Benjamin Schreck, Tobias Graz University of Technology Institute of Computer Graphics and Knowledge Visualization Austria University of Chile Department of Computer Science Chile University of Graz Institute of Classics Austria

ISBN: (纸本)9783038681786

Among various ancient cultures it was common practice to adorn pottery artifacts with lavish surface decoration. While the applied painting styles, color schemes and displayed mythological content may vary greatly, the presence of simple patterns which appear in a repetitive manner can be observed across civilizations and periods. Such pattern sequences generally are arranged in a structured manner in ornament bands or columns that extend over the entire surface of the object. Due to the poor conservation state of many cultural heritage objects, parts of the surface are oftentimes badly damaged or missing altogether. Yet, if the majority of a pattern sequence is preserved, this information can be leveraged to approximate its missing parts. We present an approach that allows the fully automatic determination of the generation rule inherent to a repetitive surface pattern. Based on this generation rule and the preserved patterns from the same pattern class we propose a workflow for reconstruct missing or damaged parts of the surface painting. We evaluate our approach by applying it to a selection of pottery from ancient Peruvian and Greek cultures, showing that our automatic approach is able to handle a variety of problem cases. © ISVR,2023 *** rights reserved

关键词： computer vision

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