[ { "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": "Gall2021", "type_id": "inproceedings", "tu_id": 300119, "repositum_id": "20.500.12708/58609", "title": "ImNDT: Immersive Workspace for the Analysis of Multidimensional Material Data From Non-Destructive Testing", "date": "2021-12-08", "abstract": "An analysis of large multidimensional volumetric data as generated by non-destructive testing (NDT) techniques, e.g., X-ray computed tomography (XCT), can hardly be evaluated using standard 2D visualization techniques on desktop monitors. The analysis of fiber-reinforced polymers (FRPs) is currently a time-consuming and cognitively demanding task, as FRPs have a complex spatial structure, consisting of several hundred thousand fibers, each having more than twenty different extracted features. This paper presents ImNDT, a novel visualization system, which offers material experts an immersive exploration of multidimensional secondary data of FRPs. Our system is based on a virtual reality (VR) head-mounted device (HMD) to enable fluid and natural explorations through embodied navigation, the avoidance of menus, and manual mode switching. We developed immersive visualization and interaction methods tailored to the characterization of FRPs, such as a Model in Miniature, a similarity network, and a histo-book. An evaluation of our techniques with domain experts showed advantages in discovering structural patterns and similarities. Especially novices can strongly benefit from our intuitive representation and spatial rendering.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 530, "image_height": 372, "name": "Gall2021-Image.JPG", "type": "image/jpeg", "size": 41905, "path": "Publication:Gall2021", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Gall2021/Gall2021-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/Gall2021/Gall2021-Image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1355, 166, 611 ], "booktitle": "Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology", "cfp": { "name": "CfP VRST 2021.pdf", "type": "application/pdf", "error": "0", "size": "94330", "orig_name": "CfP VRST 2021.pdf", "ext": "pdf" }, "doi": "10.1145/3489849.3489851", "event": "27th ACM Symposium on Virtual Reality Software and Technology", "open_access": "yes", "pages": "11", "pages_from": "1", "pages_to": "11", "publisher": "ACM Press", "research_areas": [], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 530, "image_height": 372, "name": "Gall2021-Image.JPG", "type": "image/jpeg", "size": 41905, "path": "Publication:Gall2021", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Gall2021/Gall2021-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/Gall2021/Gall2021-Image:thumb{{size}}.png" }, { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": true, "access": "public", "name": "Gall2021-Paper.pdf", "type": "application/pdf", "size": 5992354, "path": "Publication:Gall2021", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Gall2021/Gall2021-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/Gall2021/Gall2021-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Gall2021/", "__class": "Publication" }, { "id": "Gall2020", "type_id": "masterthesis", "tu_id": null, "repositum_id": "20.500.12708/16616", "title": "Immersive Analytics of Multidimensional Volumetric Data", "date": "2020-11-24", "abstract": "Understanding and interpreting volumetric multidimensional data is a complex and cognitively demanding task. Especially in the field of material science the exploration of large spatial data is crucial. Non-destructive testing (NDT) plays an essential role in industrial production, especially in the field of material and component testing, regarding the analysis, visualization, and optimization of new, highly complex material systems such as fiber composites. In order to support the increasing demands on these materials and components of the future in industrial applications, extensive inspections and controls are essential. NDT inspection data generated by imaging techniques such as X-ray computed tomography (XCT) include 2D images, volumetric models, and derived high-dimensional data spaces. They can rarely, or only to a limited extent, be evaluated on desktop monitors using standard 2D visualization techniques. Therefore, novel immersive visualization and interaction techniques using Virtual Reality (VR) were developed in this thesis to investigate highly complex, heterogeneous material systems. We present a novel technique called \"Model in Miniature\" for an effective and interactive exploration and visual analysis of fiber characteristics. Furthermore, we combine different approaches like exploded views, histograms, and node-link diagrams to provide unique insights into the composite materials. Using embodied interaction and navigation, and enhancing the user’s abilities, previously impossible insights into the most complex material structures are possible. We use the latest findings from the field of Immersive Analytics to make the spatial data more comprehensible and test the results in a qualitative study with domain experts. The evaluation of our techniques has shown positive results, which indicate the benefits of an immersive analysis of composite materials and the exploration of overall high-dimensional volumes. The insights gained therefore represent an important step towards the further development of future immersive analysis platforms.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 502, "image_height": 298, "name": "Gall2020-Image.JPG", "type": "image/jpeg", "size": 31463, "path": "Publication:Gall2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Gall2020/Gall2020-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/Gall2020/Gall2020-Image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1355 ], "co_supervisor": [ 611 ], "date_end": "2020-11-24", "date_start": "2020-01-15", "diploma_examina": "2020-11-24", "matrikelnr": "01225540", "open_access": "yes", "supervisor": [ 166 ], "research_areas": [ "InfoVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 502, "image_height": 298, "name": "Gall2020-Image.JPG", "type": "image/jpeg", "size": 31463, "path": "Publication:Gall2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Gall2020/Gall2020-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/Gall2020/Gall2020-Image:thumb{{size}}.png" }, { "description": null, "filetitle": "Master Thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "Gall2020-Master Thesis.pdf", "type": "application/pdf", "size": 17524989, "path": "Publication:Gall2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Gall2020/Gall2020-Master Thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/Gall2020/Gall2020-Master Thesis:thumb{{size}}.png" }, { "description": null, "filetitle": "Poster", "main_file": false, "use_in_gallery": false, "access": "public", "name": "Gall2020-Poster.pdf", "type": "application/pdf", "size": 2277755, "path": "Publication:Gall2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Gall2020/Gall2020-Poster.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/Gall2020/Gall2020-Poster:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Gall2020/", "__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": "Gall_2017", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Comparison of Vessel Segmentation Techniques", "date": "2017-03", "abstract": "Image segmentation is an important processing step in various applications and crucial in the medical field. When a new segmentation technique is introduced, validation and evaluation are essential for medical image analysis. But the automation of these processes is still not sufficient. Many algorithms have been published but there is still no satisfying way to assess whether an algorithm produces more accurate segmentations than another. More effort is spent on the development of algorithms than on their evaluation and therefore many researchers use the less complex subjective methods. For these techniques multiple experts are needed to visually compare several segmentation results, which is a very time-consuming process. Another way of comparing different results is the supervised evaluation method. Here we need experts, who manually segment reference images, which are used for comparison. As seen in recent researches there is a need for unsupervised methods due to many applications, in which user assistance is\ninfeasible. The aim of this thesis is to provide an environment to visually and objectively evaluate segmentation results in the field of vessel segmentations. Our framework enables the comparison at voxel-level with various visualization techniques and objective measurements. These methods are meant to make the comparison more understandable for users. A subjective evaluation is realized through a comparative visualization by using a two- and three-dimensional comparison of voxels. Another general overview is provided by a maximum-intensity projection, which highlights the vessel structure. As purely\nobjective evaluation technique, various metrics are used, to assure independence from experts or a ground truth. By using these techniques this paper presents an approach for evaluating differences in medical images, which does not rely on a permanent presence of an expert.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 273, "image_height": 208, "name": "Gall_2017-image.JPG", "type": "image/jpeg", "size": 14192, "path": "Publication:Gall_2017", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Gall_2017/Gall_2017-image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/Gall_2017/Gall_2017-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1355 ], "date_end": "2017-03-08", "date_start": "2016-10-01", "matrikelnr": "1225540", "supervisor": [ 166 ], "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Bachelor Thesis", "main_file": true, "use_in_gallery": true, "access": "public", "name": "Gall_2017-Bachelor Thesis.pdf", "type": "application/pdf", "size": 13262337, "path": "Publication:Gall_2017", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Gall_2017/Gall_2017-Bachelor Thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/Gall_2017/Gall_2017-Bachelor Thesis:thumb{{size}}.png" }, { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 273, "image_height": 208, "name": "Gall_2017-image.JPG", "type": "image/jpeg", "size": 14192, "path": "Publication:Gall_2017", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Gall_2017/Gall_2017-image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/Gall_2017/Gall_2017-image:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Gall_2017/", "__class": "Publication" } ]