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22nd Eurographics/IEEE VGT Conference on visualization, EuroVis 2020

作者： Bors, C. Eichner, C. Miksch, S. Tominski, C. Schumann, H. Gschwandtner, T. Institute of Visual Computing and Human-Centered Technology TU Wien Austria Institute for Visual and Analytic Computing University of Rostock Germany

ISBN: (纸本)9783038681069

Time series segmentation is employed in various domains and continues to be a relevant topic of research. A segmentation pipeline is composed of different steps involving several parameterizable algorithms. Existing visual Analytics approaches can help experts determine appropriate parameterizations and corresponding segmentation results for a given dataset. However, the results may also be afflicted with different types of uncertainties. Hence, experts face the additional challenge of understanding the reliability of multiple alternative the segmentation results. So far, the influence of uncertainties in the context of time series segmentation could not be investigated. We present an uncertainty-aware exploration approach for analyzing large sets of multivariate time series segmentations. The approach features an overview of uncertainties and time series segmentations, while detailed exploration is facilitated by (1) a lens-based focus+context technique and (2) uncertainty-based re-arrangement. The suitability of our uncertainty-aware design was evaluated in a quantitative user study, which resulted in interesting findings of general validity. © 2020 The Author(s)

关键词： Uncertainty analysis

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Tolerating Untrustworthy Robots: Studying human Vulnerability Experience within a Privacy Scenario for Trust in Robots

Tolerating Untrustworthy Robots: Studying Human Vulnerabilit...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Glenda Hannibal Anna Dobrosovestnova Astrid Weiss Institute of Artificial Intelligence (xAI Group) Ulm University Ulm Baden-Württemberg Germany Institute of Visual Computing and Human-Centered Technology (HCI Group) TU Wien Austria

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Focusing on human experience of vulnerability in everyday life interaction scenarios is still a novel approach. So far, only a proof-of-concept online study has been conducted, and to extend this work, we present a follow-up online study. We consider in more detail how human experience of vulnerability caused by a trust violation through a privacy breach affects trust ratings in an interaction scenario with the PEPPER robot assisting with clothes shopping. We report the results from 32 survey responses and 11 semi-structured interviews. Our findings reveal the existence of the privacy paradox also for studying trust in HRI, which is a common observation describing a discrepancy between the stated privacy concerns by people and their behavior to safeguard it. Moreover, we reflect that participants considered only the added value of utility and entertainment when deciding whether or not to interact with the robot again, but not the privacy breach. We conclude that people might tolerate an untrustworthy robot even when they are feeling vulnerable in the everyday life situation of clothes shopping.

关键词： Entertainment industry Focusing Robot sensing systems Privacy breach Behavioral sciences Interviews

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Fast multi-view rendering for real-time applications

Fast multi-view rendering for real-time applications

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20th Eurographics Symposium on Parallel Graphics and visualization, EGPGV 2020

作者： Unterguggenberger, Johannes Kerbl, Bernhard Steinberger, Markus Schmalstieg, Dieter Wimmer, Michael TU Wien Institute of Visual Computing and Human-Centered Technology Austria Graz University of Technology Austria

ISBN: (纸本)9783038681076

Efficient rendering of multiple views can be a critical performance factor for real-time rendering applications. Generating more than one view multiplies the amount of rendered geometry, which can cause a huge performance impact. Minimizing that impact has been a target of previous research and GPU manufacturers, who have started to equip devices with dedicated acceleration units. However, vendor-specific acceleration is not the only option to increase multi-view rendering (MVR) performance. Available graphics API features, shader stages and optimizations can be exploited for improved MVR performance, while generally offering more versatile pipeline configurations, including the preservation of custom tessellation and geometry shaders. In this paper, we present an exhaustive evaluation of MVR pipelines available on modern GPUs. We provide a detailed analysis of previous techniques, hardware-accelerated MVR and propose a novel method, leading to the creation of an MVR catalogue. Our analyses cover three distinct applications to help gain clarity on overall MVR performance characteristics. Our interpretation of the observed results provides a guideline for selecting the most appropriate one for various use cases on different GPU architectures. © 2020 The Author(s) Eurographics Proceedings © 2020 The Eurographics Association.

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FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection

arXiv

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arXiv 2024年

作者： Gutwein, Simon Kampel, Martin Taschner-Mandl, Sabine Licandro, Roxane St. Anna Children’s Cancer Research Institute Vienna Austria TU Wien Institute of Visual Computing and Human-Centered Technology Computer Vision Lab Vienna Austria Medical University of Vienna Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research ELIA Group Vienna Austria

Detecting genetic aberrations is crucial in cancer diagnosis, typically through fluorescence in situ hybridization (FISH). However, existing FISH image classification methods face challenges due to signal variability, the need for costly manual annotations and fail to adequately address the intrinsic uncertainty. We introduce a novel approach that leverages synthetic images to eliminate the requirement for manual annotations and utilizes a joint contrastive and classification objective for training to account for inter-class variation effectively. We demonstrate the superior generalization capabilities and uncertainty calibration of our method, which is trained on synthetic data, by testing it on a manually annotated dataset of real-world FISH images. Our model offers superior calibration in terms of classification accuracy and uncertainty quantification with a classification accuracy of 96.7% among the 50% most certain cases. The presented end-to-end method reduces the demands on personnel and time and improves the diagnostic workflow due to its accuracy and adaptability. All code and data is publicly accessible at: https://***/SimonBon/FISHing. © 2024, CC BY-NC-ND.

关键词： Contrastive Learning

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Subjective assessments of legibility in ancient manuscript images - The SALAMI dataset

arXiv

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arXiv 2021年

作者： Brenner, Simon Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered technology TU Wien Vienna Austria

The research field concerned with the digital restoration of degraded written heritage lacks a quantitative metric for evaluating its results, which prevents the comparison of relevant methods on large datasets. Thus, we introduce a novel dataset of Subjective Assessments of Legibility in Ancient Manuscript Images (SALAMI) to serve as a ground truth for the development of quantitative evaluation metrics in the field of digital text restoration. This dataset consists of 250 images of 50 manuscript regions with corresponding spatial maps of mean legibility and uncertainty, which are based on a study conducted with 20 experts of philology and paleography. As this study is the first of its kind, the validity and reliability of its design and the results obtained are motivated statistically: we report a high intra- and inter-rater agreement and show that the bulk of variation in the scores is introduced by the images regions observed and not by controlled or uncontrolled properties of participants and test environments, thus concluding that the legibility scores measured are valid attributes of the underlying images. Copyright © 2021, The Authors. All rights reserved.

关键词： Restoration

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A study on training data selection for object detection in nighttime traffic scenes

A study on training data selection for object detection in n...

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2020 Autonomous Vehicles and Machines Conference, AVM 2020

作者： Unger, Astrid Gelautz, Margrit Seitner, Florian Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Emotion3D GmbH Vienna Austria

With the growing demand for robust object detection algorithms in self-driving systems, it is important to consider the varying lighting and weather conditions in which cars operate all year round. The goal of our work is to gain a deeper understanding of meaningful strategies for selecting and merging training data from currently available databases and self-annotated videos in the context of automotive night scenes. We retrain an existing Convolutional Neural Network (YOLOv3) to study the influence of different training dataset combinations on the final object detection results in nighttime and low-visibility traffic scenes. Our evaluation shows that a suitable selection of training data from the GTSRD, VIPER, and BDD databases in conjunction with selfrecorded night scenes can achieve an mAP of 63,5% for ten object classes, which is an improvement of 16,7% when compared to the performance of the original YOLOv3 network on the same test set. © 2020, Society for Imaging Science and technology.

关键词： Object detection

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visual Parameter Selection for Spatial Blind Source Separation

arXiv

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arXiv 2021年

作者： Piccolotto, N. Bögl, M. Muehlmann, C. Nordhausen, K. Filzmoser, P. Miksch, S. TU Wien Institute of Visual Computing and Human-Centered Technology Austria TU Wien Institute of Statistics and Mathematical Methods in Economics Austria University of Jyväskylä Finland

Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are integral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non-expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data. Copyright © 2021, The Authors. All rights reserved.

关键词： visualization

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TBSSvis:visual analytics for Temporal Blind Source Separation

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visual Informatics 2022年第4期6卷 51-66页

作者： Nikolaus Piccolotto Markus Bögl Theresia Gschwandtner Christoph Muehlmann Klaus Nordhausen Peter Filzmoser Silvia Miksch TU Wien Institute of Visual Computing&Human-Centered TechnologyFavoritenstrasse 9–11A-1040 ViennaAustria Erste Group Bank AG Am Belvedere 1A-1100 ViennaAustria TU Wien Institute of Statistics and Mathematical Methods in EconomicsWiedner Hauptstrasse 8–10A-1040 ViennaAustria University of Jyväskylä Department of Mathematics and StatisticsFI40014 JyväskyläFinland

Temporal Blind Source Separation(TBSS)is used to obtain the true underlying processes from noisy temporal multivariate data,such as *** has similarities to Principal Component Analysis(PCA)as it separates the input data into univariate components and is applicable to suitable datasets from various domains,such as medicine,finance,or civil *** TBSS’s broad applicability,the involved tasks are not well supported in current tools,which offer only text-based interactions and single static *** are limited in analyzing and comparing obtained results,which consist of diverse data such as matrices and sets of time ***,parameter settings have a big impact on separation performance,but as a consequence of improper tooling,analysts currently do not consider the whole parameter *** propose to solve these problems by applying visual analytics(VA)*** primary contribution is a design study for TBSS,which so far has not been explored by the visualization *** developed a task abstraction and visualization design in a user-centered design ***-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary *** present TBSSvis,an interactive web-based VA prototype,which we evaluated extensively in two interviews with five TBSS *** and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.

关键词： Blind source separation Ensemble visualization visual analytics Parameter space exploration

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visual Analytics Meets Process Mining: Challenges and Opportunities

Visual Analytics Meets Process Mining: Challenges and Opport...

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International Conference on Process Mining (ICPM)

作者： Silvia Miksch Institute of Visual Computing and Human-Centered Technology Vienna University of Technology (TU Wien) Favoritenstraße 9-11/193-07 Vienna Austria

visual Analytics integrates the outstanding capabilities of humans in terms of visual information exploration with the enormous processing power of computers to form a powerful knowledge discovery environment. In other words, visual Analytics is the science of analytical reasoning facilitated by interactive interfaces and captures the information discovery process keeping the human in the loop. Process Mining aims to extract information and knowledge from event logs to discover, monitor, and improve processes in a variety of application domains. Such event data or traces of activities often possess data quality issues as well as exhibit unexpected behavior and complex relations. Consequently, before and during the implementation of interactive (automated or semi-automated) analysis methods, such as Process Mining algorithms, the analyst needs to explore, investigate, and understand the data at hand in order to decide which analysis methods might be appropriate. The combination of interactive visual data analyses and exploration with Process Mining algorithms makes complex information structures more comprehensible and facilitates new insights. In this talk, I will illustrate the concepts of visual Analytics, how visual Analytics combined with Process Mining techniques supports to extract more insights from complex event data, and elaborate about the challenges and opportunities for analyzing process data with visual Analytics methods. Various examples will illustrate what has been achieved so far and show possible future directions and challenges.

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MCCNet: Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images 25th

MCCNet: Multi-Color Cascade Network with Weight Transfer for...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Frisky, Aufaclav Zatu Kusuma Putranto, Andi Zambanini, Sebastian Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Vienna Austria Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Faculty of Cultural Science Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9783030687861

Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. Thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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