[ { "id": "gall-2025-marv", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/223667", "title": "MARV: Multiview Augmented Reality Visualisation for Exploring Rich Material Data", "date": "2025-09", "abstract": "Rich material data is complex, large and heterogeneous, integrating primary and secondary non-destructive testing data for spatial, spatio-temporal, as well as high-dimensional data analyses. Currently, materials experts mainly rely on conventional desktop-based systems using 2D visualisation techniques, which render respective analyses a time-consuming and mentally demanding challenge. MARV is a novel immersive visual analytics system, which makes analyses of such data more effective and engaging in an augmented reality setting. For this purpose, MARV includes three newly designed visualisation techniques: MDD Glyphs with a Skewness Kurtosis Mapper, Temporal Evolution Tracker, and Chrono Bins, facilitating interactive exploration and comparison of multidimensional distributions of attribute data from multiple time steps. A qualitative evaluation conducted with materials experts in a real-world case study demonstrates the benefits of the proposed visualisation techniques. This evaluation revealed that combining spatial and abstract data in an immersive environment improves their analytical capabilities and facilitates the identification of patterns, anomalies, as well as changes over time.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1355, 1354, 166, 611 ], "articleno": "e70150", "doi": "10.1111/cgf.70150", "issn": "1467-8659", "journal": "Computer Graphics Forum", "number": "6", "pages": "15", "publisher": "WILEY", "volume": "44", "research_areas": [ "VisMat", "VR" ], "keywords": [ "virtual environments", "augmented reality", "immersive analytics", "scientific visualisation", "visualization", "visual analytics" ], "weblinks": [], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/gall-2025-marv/", "__class": "Publication" }, { "id": "heim-2024-accustripes", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/197495", "title": "AccuStripes: Visual exploration and comparison of univariate data distributions using color and binning", "date": "2024-04", "abstract": "Understanding and analyzing univariate distributions of data in terms of their shapes as well as their specific characteristics, regarding gaps, spikes, or outliers, is crucial in many scientific disciplines. In this paper, we propose a design space composed of the visual channels position and color for representing accumulated distributions. The designs are a mixture of color-coded stripes with density lines. The width and coloring of the stripes is based on the applied binning technique. In a crowd-sourced experiment we explore a subspace, called the AccuStripes (i.e., “accumulated stripes”) design space, consisting of nine representations. These AccuStripes designs integrate three composition strategies (color only, overlay, filled curve) with three binning techniques, one uniform (UB) and two adaptive methods, namely Bayesian Blocks (BB) and Jenks’ Natural Breaks (NB). We evaluate the accuracy, efficiency, and confidence ratings of the nine AccuStripes designs for structural estimation and comparison tasks. Across all study tasks, the overlay composition was found to be most accurate and preferred by observers. Furthermore, the results demonstrate that while no binning method performed best in both identification and comparison, detection of structures using adaptive binning was the most accurate one. For validation we compared the best AccuStripes’ design, i.e., the overlay composition, to line charts. Our results show that the AccuStripes’ design outperformed the line charts in accuracy for all study tasks.", "authors_et_al": false, "substitute": null, "main_image": { "description": "AccuStripes - Graphical Abstract", "filetitle": "accuStripes", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1333, "image_height": 428, "name": "heim-2024-accustripes-accuStripes.png", "type": "image/png", "size": 366335, "path": "Publication:heim-2024-accustripes", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/heim-2024-accustripes/heim-2024-accustripes-accuStripes.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/heim-2024-accustripes/heim-2024-accustripes-accuStripes:thumb{{size}}.png" }, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1354, 1355, 1110, 166, 611 ], "articleno": "103906", "doi": "10.1016/j.cag.2024.103906", "issn": "1873-7684", "journal": "COMPUTERS & GRAPHICS-UK", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "119", "research_areas": [ "InfoVis" ], "keywords": [ "Adaptive binning", "Crowd-sourced experiment", "Univariate data distributions", "Visual analysis" ], "weblinks": [ { "href": "https://www.sciencedirect.com/science/article/pii/S0097849324000414", "caption": "paper", "description": null, "main_file": 1 } ], "files": [ { "description": "AccuStripes - Graphical Abstract", "filetitle": "accuStripes", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1333, "image_height": 428, "name": "heim-2024-accustripes-accuStripes.png", "type": "image/png", "size": 366335, "path": "Publication:heim-2024-accustripes", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/heim-2024-accustripes/heim-2024-accustripes-accuStripes.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/heim-2024-accustripes/heim-2024-accustripes-accuStripes:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/heim-2024-accustripes/", "__class": "Publication" }, { "id": "Heim_2021", "type_id": "journalpaper_notalk", "tu_id": 300188, "repositum_id": "20.500.12708/138542", "title": "CoSi: Visual Comparison of Similarities in High-Dimensional Data Ensembles", "date": "2021-10-10", "abstract": "Comparative analysis of multivariate datasets, e.g. of advanced materials regarding the characteristics of internal structures (fibers, pores, etc.), is of crucial importance in various scientific disciplines. Currently domain experts in materials science mostly rely on sequential comparison of data using juxtaposition. Our work assists domain experts to perform detailed comparative analyses of large ensemble data in materials science applications. For this purpose, we developed a comparative visualization framework, that includes a tabular overview and three detailed visualization techniques to provide a holistic view on the similarities in the ensemble. We demonstrate the applicability of our framework on two specific usage scenarios and verify its techniques using a qualitative user study with 12 material experts. The insights gained from our work represent a significant advancement in the field of comparative material analysis of high-dimensional data. Our framework provides experts with a novel perspective on the data and eliminates the need for time-consuming sequential exploration of numerical data.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 765, "image_height": 399, "name": "Heim_2021-Image.JPG", "type": "image/jpeg", "size": 69697, "path": "Publication:Heim_2021", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Heim_2021/Heim_2021-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/Heim_2021/Heim_2021-Image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1354, 166, 611 ], "doi": "CoSi: Visual Comparison of Similarities in High-Dimensional Data Ensembles", "first_published": "2021-10-10", "journal": "VMV: Vision, Modeling, and Visualization", "open_access": "yes", "pages_from": "1", "pages_to": "8", "research_areas": [ "InfoVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 765, "image_height": 399, "name": "Heim_2021-Image.JPG", "type": "image/jpeg", "size": 69697, "path": "Publication:Heim_2021", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Heim_2021/Heim_2021-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/Heim_2021/Heim_2021-Image:thumb{{size}}.png" }, { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": true, "access": "public", "name": "Heim_2021-Paper.pdf", "type": "application/pdf", "size": 926914, "path": "Publication:Heim_2021", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Heim_2021/Heim_2021-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/Heim_2021/Heim_2021-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Heim_2021/", "__class": "Publication" }, { "id": "Heim_2020", "type_id": "masterthesis", "tu_id": null, "repositum_id": "20.500.12708/16606", "title": "Visual Comparison of Multivariate Data Ensembles", "date": "2020-11-24", "abstract": "In safety-critical areas such as aeronautics, but also in other sectors such as the leisure industry, the advancement of respective products is largely driven by the improvement of the materials used. In order to analyze the targeted properties of these new materials, data of the internal structures is generated, using imaging techniques such as X-ray computed tomography (XCT), which is then analyzed in detail using segmentation and quantification algorithms. For materials scientists, the exact design of the internal structures is crucial for the characterization of materials and a comparison of several material candidates based on their characteristics is therefore indispensable for the investigation of di˙erent manufacturing and optimization processes or property behavior.\nCurrently, material scientists are dependent on sequential comparisons when analyzing several material candidates. Distributions of the individual attributes across the material systems need to be compared, which is why this task is typically cognitively demanding, time consuming, and thus error-prone. This work aims to support domain experts in their daily tasks of analysing large ensembles of material data. For this purpose we developed a comparative visualization framework that provides a holistic picture of similarities and dissimilarities in the data by means of an overview visualization and three detailed visualization techniques. Using the dimension reduction method Multidimensional Scaling, the individual structures are summarized and rendered in a table-based visualization technique called Histogram-Table. Information, describing in which attributes the structures are most similar as well as their exact characteristics, is evaluated by statistical calculations, the results of which are visualized in a bar chart and box plot. Finally, the linear correlations between the individual characteristics can be explored in a correlation map. We present the usability of this visualization system by means of three concrete usage scenarios and verify its applicability by means of a qualitative study with 12 material experts. The knowledge gained from our work represents a significant step in the field of comparative material analysis of high-dimensional data and supports experts in making their work easier and more eÿcient.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 262, "image_height": 520, "name": "Heim_2020-Image.JPG", "type": "image/jpeg", "size": 29877, "path": "Publication:Heim_2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Heim_2020/Heim_2020-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/Heim_2020/Heim_2020-Image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1354 ], "co_supervisor": [ 611 ], "date_end": "2020-11-24", "date_start": "2020-01-12", "diploma_examina": "2020-11-24", "matrikelnr": "01226809", "open_access": "yes", "supervisor": [ 166 ], "research_areas": [ "IllVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 262, "image_height": 520, "name": "Heim_2020-Image.JPG", "type": "image/jpeg", "size": 29877, "path": "Publication:Heim_2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Heim_2020/Heim_2020-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/Heim_2020/Heim_2020-Image:thumb{{size}}.png" }, { "description": null, "filetitle": "Master Thesis", "main_file": true, "use_in_gallery": true, "access": "public", "name": "Heim_2020-Master Thesis.pdf", "type": "application/pdf", "size": 20005273, "path": "Publication:Heim_2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Heim_2020/Heim_2020-Master Thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/Heim_2020/Heim_2020-Master Thesis:thumb{{size}}.png" }, { "description": null, "filetitle": "Poster", "main_file": true, "use_in_gallery": true, "access": "public", "name": "Heim_2020-Poster.pdf", "type": "application/pdf", "size": 889017, "path": "Publication:Heim_2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Heim_2020/Heim_2020-Poster.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/Heim_2020/Heim_2020-Poster:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Heim_2020/", "__class": "Publication" }, { "id": "heim-gall-2020-dde", "type_id": "studentproject", "tu_id": null, "repositum_id": null, "title": "DDE - Dynamic Data Explorer: Dynamic data exploration in a collaborative spatial-aware environment", "date": "2020-01", "abstract": "Collaborative decision-making has become an integral part of the analysis process aiming to get insight into multivariate\ndata. To further encourage this workflow numerous co-located, multi-user systems have been developed consisting of large\nmulti-touch screens or interactive tabletops. But such frameworks are typically expensive and unavailable outside dedicated\nenvironments as for example laboratories. Therefore we developed the Dynamic Data Explorer, short DDE, a multi-user system\nthat enables users to join, in an ad-hoc manner, with their own mobile devices. Since forming groups should be possible in\nvarious locations, the tracking system, enabling spatial awareness of the devices, has to be light-weight and small. Near Field\nCommunication (NFC) is a widespread transmission technology which fulfils these properties and is used in our framework to\nenable different side-by-side arrangements of devices. This allows users to explore multivarate data visualizations on a system\nwhere the number of devices and their set-up can be modified at all times.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "setup", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2220, "image_height": 1900, "name": "heim-gall-2020-dde-setup.jpg", "type": "image/jpeg", "size": 1379498, "path": "Publication:heim-gall-2020-dde", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/heim-gall-2020-dde/heim-gall-2020-dde-setup.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/heim-gall-2020-dde/heim-gall-2020-dde-setup:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1354, 1355 ], "date_end": "2020-01", "date_start": "2019-04", "matrikelnr": "1226809, 1225540", "supervisor": [ 1110, 1643 ], "research_areas": [ "InfoVis" ], "keywords": [], "weblinks": [ { "href": "https://github.com/nonsens949/Practicum/wiki", "caption": "GitHub", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "report", "main_file": false, "use_in_gallery": false, "access": "public", "name": "heim-gall-2020-dde-report.pdf", "type": "application/pdf", "size": 1583282, "path": "Publication:heim-gall-2020-dde", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/heim-gall-2020-dde/heim-gall-2020-dde-report.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/heim-gall-2020-dde/heim-gall-2020-dde-report:thumb{{size}}.png" }, { "description": null, "filetitle": "setup", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2220, "image_height": 1900, "name": "heim-gall-2020-dde-setup.jpg", "type": "image/jpeg", "size": 1379498, "path": "Publication:heim-gall-2020-dde", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/heim-gall-2020-dde/heim-gall-2020-dde-setup.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/heim-gall-2020-dde/heim-gall-2020-dde-setup:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/heim-gall-2020-dde/", "__class": "Publication" }, { "id": "Heim-2017", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Semiautomated Editing of Vessel Segmentation Masks", "date": "2017-03", "abstract": "This thesis describes a technique for editing segmentation results of vessels, which should enhance usage and reduce work duration for physicians by using a simple and fast way of interaction. Moreover also a quick calculation of an accurate result was of primary interest. Since vascular structures are vulnerable to diseases, vessels are the main focus of this thesis. Nowadays, Image Analysis is able to facilitate the medical diagnosis procedure.\nSince stroke treatment is time-crucial, appropriate algorithms should be fast and enable an accurate depiction of the arteries to simplify the diagnostic process. However, because automatic segmentation is often quite inaccurate and manual segmentation is tedious, neither of these two methods alone is often adequate for usage. Because of this we suggest to combine the fast automatic segmentation and the exact manual editing done by clinical experts. To reduce effort and working time of the medical staff, this thesis describes different techniques, which were developed to modify and, more importantly, to improve\nthe segmentation results. The segmentation mask can be altered as its components can be separately removed and independent elements can be connected. A framework was implemented, with which a user is able to perform these tasks interactively. The deletion process is supported by various metrics, which enable the search and removal of similar structures. Also this framework assists the reconnection of vessels by finding the most likely connection by the means of image intensities and their gradients. The main goal of this thesis was to facilitate and accelerate the editing process by implementing fast semi-automatic algorithms. Intuitive interaction methods also had a major impact on the design.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 463, "image_height": 400, "name": "Heim-2017-image.JPG", "type": "image/jpeg", "size": 41540, "path": "Publication:Heim-2017", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Heim-2017/Heim-2017-image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/Heim-2017/Heim-2017-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1354 ], "date_end": "2017-03-08", "date_start": "2016-10-01", "matrikelnr": "1226809", "supervisor": [ 166 ], "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Bachelor Thesis", "main_file": true, "use_in_gallery": true, "access": "public", "name": "Heim-2017-Bachelor Thesis.pdf", "type": "application/pdf", "size": 6896211, "path": "Publication:Heim-2017", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Heim-2017/Heim-2017-Bachelor Thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/Heim-2017/Heim-2017-Bachelor Thesis:thumb{{size}}.png" }, { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 463, "image_height": 400, "name": "Heim-2017-image.JPG", "type": "image/jpeg", "size": 41540, "path": "Publication:Heim-2017", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Heim-2017/Heim-2017-image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/Heim-2017/Heim-2017-image:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Heim-2017/", "__class": "Publication" } ]