[ { "id": "amirkhanov2020visual", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Visual Analytics in Dental Aesthetics", "date": "2020", "abstract": "Dental healthcare increasingly employs computer-aided design software,\nto provide patients with high-quality dental prosthetic devices. In\nmodern dental reconstruction, dental technicians address the unique\nanatomy of each patient individually, by capturing the dental impression\nand measuring the mandibular movements. Subsequently, dental technicians\ndesign a custom denture that fits the patient from a functional point of\nview. The current workflow does not include a systematic analysis of\naesthetics, and dental technicians rely only on an aesthetically\npleasing mock-up that they discuss with the patient, and on their\nexperience. Therefore, the final denture aesthetics remain unknown until\nthe dental technicians incorporate the denture into the patient. In this\nwork, we present a solution that integrates aesthetics analysis into the\nfunctional workflow of dental technicians. Our solution uses a video\nrecording of the patient, to preview the denture design at any stage of\nthe denture design process. We present a teeth pose estimation technique\nthat enables denture preview and a set of linked visualizations that\nsupport dental technicians in the aesthetic design of dentures. These\nvisualizations assist dental technicians in choosing the most\naesthetically fitting preset from a library of dentures, in identifying\nthe suitable denture size, and in adjusting the denture position. We\ndemonstrate the utility of our system with four use cases, explored by a\ndental technician. Also, we performed a quantitative evaluation for\nteeth pose estimation, and an informal usability evaluation, with\npositive outcomes concerning the integration of aesthetics analysis into\nthe functional workflow.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1170, 660, 925, 1628, 1629, 166, 869 ], "doi": "https://doi.org/10.1111/cgf.14174", "journal": "Computer Graphics Forum", "number": "7", "pages": "635–646", "pages_from": "635", "pages_to": "646", "volume": "39", "research_areas": [], "keywords": [ "Applied computing -> Life and medical sciences", "Human-centered computing -> Visualization application domains" ], "weblinks": [ { "href": "https://amirkhanov.net/visual-analytics-in-dental-aesthetics/", "caption": "Official page", "description": null, "main_file": 0 } ], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/amirkhanov2020visual/", "__class": "Publication" }, { "id": "waldner-2017-vph", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Exploring Visual Prominence of Multi-Channel Highlighting in Visualizations", "date": "2017-05", "abstract": "Visualizations make rich use of multiple visual channels so that there are few resources left to make selected focus elements visually\ndistinct from their surrounding context. A large variety of highlighting techniques for visualizations has been presented in the past,\nbut there has been little systematic evaluation of the design space of highlighting. We explore highlighting from the perspective\nof visual marks and channels – the basic building blocks of visualizations that are directly controlled by visualization designers.\nWe present the results from two experiments, exploring the visual prominence of highlighted marks in scatterplots: First, using\nluminance as a single highlight channel, we found that visual prominence is mainly determined by the luminance difference between\nthe focus mark and the brightest context mark. The brightness differences between context marks and the overall brightness level\nhave negligible influence. Second, multi-channel highlighting using luminance and blur leads to a good trade-off between highlight\neffectiveness and aesthetics. From the results, we derive a simple highlight model to balance highlighting across multiple visual\nchannels and focus and context marks, respectively.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 160, "image_height": 124, "name": "waldner-2017-vph-.png", "type": "image/png", "size": 20447, "path": "Publication:waldner-2017-vph", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1110, 925, 166 ], "booktitle": "Spring Conference on Computer Graphics 2017", "lecturer": [ 1110 ], "research_areas": [ "InfoVis", "Perception" ], "keywords": [ "information visualization", "highlighting", "focus+context", "visual prominence" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 160, "image_height": 124, "name": "waldner-2017-vph-.png", "type": "image/png", "size": 20447, "path": "Publication:waldner-2017-vph", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "waldner-2017-vph-paper.pdf", "type": "application/pdf", "size": 1851221, "path": "Publication:waldner-2017-vph", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-paper:thumb{{size}}.png" }, { "description": "Details about experiment design and results. ", "filetitle": "supplemental material", "main_file": false, "use_in_gallery": false, "access": "public", "name": "waldner-2017-vph-supplemental material.pdf", "type": "application/pdf", "size": 1396255, "path": "Publication:waldner-2017-vph", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-supplemental material.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-supplemental material:thumb{{size}}.png" } ], "projects_workgroups": [ "deskollage" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/", "__class": "Publication" }, { "id": "miao_tvcg_2017", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Placenta Maps: In Utero Placental Health Assessment of the Human Fetus", "date": "2017", "abstract": "null", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1236, "image_height": 1198, "preview_image_width": 1236, "preview_image_height": 1198, "name": "miao_tvcg_2017-image.png", "type": "image/png", "size": 588467, "path": "Publication:miao_tvcg_2017", "preview_name": "miao_tvcg_2017-image:preview.png", "preview_type": "image/png", "preview_size": 588467, "url": "https://www.cg.tuwien.ac.at/research/publications/2017/miao_tvcg_2017/miao_tvcg_2017-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/miao_tvcg_2017/miao_tvcg_2017-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1263, 869, 925, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 563, 1432, 166 ], "date_from": "2016-01-01", "date_to": "2017-04-21", "event": "Pacific Vis 2017", "journal": "IEEE Transactions on Visualization and Computer Graphics", "lecturer": [ 1263 ], "number": "6", "pages_from": "1612", "pages_to": "1623", "protocol": "null", "volume": "23", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1236, "image_height": 1198, "preview_image_width": 1236, "preview_image_height": 1198, "name": "miao_tvcg_2017-image.png", "type": "image/png", "size": 588467, "path": "Publication:miao_tvcg_2017", "preview_name": "miao_tvcg_2017-image:preview.png", "preview_type": "image/png", "preview_size": 588467, "url": "https://www.cg.tuwien.ac.at/research/publications/2017/miao_tvcg_2017/miao_tvcg_2017-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/miao_tvcg_2017/miao_tvcg_2017-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "miao_tvcg_2017-paper.pdf", "type": "application/pdf", "size": 17621975, "path": "Publication:miao_tvcg_2017", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/miao_tvcg_2017/miao_tvcg_2017-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/miao_tvcg_2017/miao_tvcg_2017-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/miao_tvcg_2017/", "__class": "Publication" }, { "id": "karimov-2016-GIVE", "type_id": "phdthesis", "tu_id": null, "repositum_id": null, "title": "Guided Interactive Volume Editing in Medicine", "date": "2016-06", "abstract": "Various medical imaging techniques, such as Computed Tomography, Magnetic Resonance Imaging, Ultrasonic Imaging, are now gold standards in the diagnosis of different diseases.\nThe diagnostic process can be greatly improved with the aid of automatic and interactive analysis tools, which, however, require certain prerequisites in order to operate.\nSuch analysis tools can, for example, be used for pathology assessment, various standardized measurements, treatment and operation planning.\nOne of the major requirements of such tools is the segmentation mask of an object-of-interest.\nHowever, the segmentation of medical data remains subject to errors and mistakes.\nOften, physicians have to manually inspect and correct the segmentation results, as (semi-)automatic techniques do not immediately satisfy the required quality.\nTo this end, interactive segmentation editing is an integral part of medical image processing and visualization.\n\nIn this thesis, we present three advanced segmentation-editing techniques.\nThey are focused on simple interaction operations that allow the user to edit segmentation masks quickly and effectively.\nThese operations are based on a topology-aware representation that captures structural features of the segmentation mask of the object-of-interest.\n\nFirstly, in order to streamline the correction process, we classify segmentation defects according to underlying structural features and propose a correction procedure for each type of defect.\nThis alleviates users from manually applying the proper editing operations, but the segmentation defects still have to be located by users.\n\nSecondly, we extend the basic editing process by detecting regions that potentially contain defects.\nWith subsequently suggested correction scenarios, users are hereby immediately able to correct a specific defect, instead of manually searching for defects beforehand.\nFor each suggested correction scenario, we automatically determine the corresponding region of the respective defect in the segmentation mask and propose a suitable correction operation.\nIn order to create the correction scenarios, we detect dissimilarities within the data values of the mask and then classify them according to the characteristics of a certain type of defect.\nPotential findings are presented with a glyph-based visualization that facilitates users to interactively explore the suggested correction scenarios on different levels-of-detail.\nAs a consequence, our approach even offers users the possibility to fine-tune the chosen correction scenario instead of directly manipulating the segmentation mask, which is a time-consuming and cumbersome task.\n\nThird and finally, we guide users through the multitude of suggested correction scenarios of the entire correction process.\nAfter statistically evaluating all suggested correction scenarios, we rank them according to their significance of dissimilarities, offering fine-grained editing capabilities at a user-specified level-of-detail.\nAs we visually convey this ranking in a radial layout, users can easily spot and select the most (or the least) dissimilar correction scenario, which improves the segmentation mask mostly towards the desired result.\n\nAll techniques proposed within this thesis have been evaluated by collaborating radiologists.\nWe assessed the usability, interaction aspects, the accuracy of the results and the expenditure of time of the entire correction process.\nThe outcome of the assessment showed that our guided volume editing not only leads to acceptable segmentation results with only a few interaction steps, but also is applicable to various application scenarios.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Image", "filetitle": "Image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 925, "image_height": 342, "name": "karimov-2016-GIVE-Image.png", "type": "image/png", "size": 301144, "path": "Publication:karimov-2016-GIVE", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-GIVE/karimov-2016-GIVE-Image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-GIVE/karimov-2016-GIVE-Image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 925 ], "date_end": "2016-06", "date_start": "2012-03", "reviewer_1": [ 161 ], "reviewer_2": [ 563 ], "rigorosum": "2016-06-28", "supervisor": [ 166 ], "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": "Abstract", "filetitle": "Abstract", "main_file": true, "use_in_gallery": false, "access": "public", "name": "karimov-2016-GIVE-Abstract.pdf", "type": "application/csv", "size": 60623, "path": "Publication:karimov-2016-GIVE", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-GIVE/karimov-2016-GIVE-Abstract.pdf", "thumb_image_sizes": [] }, { "description": "Image", "filetitle": "Image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 925, "image_height": 342, "name": "karimov-2016-GIVE-Image.png", "type": "image/png", "size": 301144, "path": "Publication:karimov-2016-GIVE", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-GIVE/karimov-2016-GIVE-Image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-GIVE/karimov-2016-GIVE-Image:thumb{{size}}.png" }, { "description": "Slides", "filetitle": "Slides", "main_file": false, "use_in_gallery": false, "access": "public", "name": "karimov-2016-GIVE-Slides.pptx", "type": "application/vnd.openxmlformats-officedocument.presentationml.presentation", "size": 40948061, "path": "Publication:karimov-2016-GIVE", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-GIVE/karimov-2016-GIVE-Slides.pptx", "thumb_image_sizes": [] }, { "description": "Thesis", "filetitle": "Thesis", "main_file": false, "use_in_gallery": false, "access": "public", "name": "karimov-2016-GIVE-Thesis.pdf", "type": "application/csv", "size": 80499879, "path": "Publication:karimov-2016-GIVE", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-GIVE/karimov-2016-GIVE-Thesis.pdf", "thumb_image_sizes": [] } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-GIVE/", "__class": "Publication" }, { "id": "karimov-2016-SD", "type_id": "techreport", "tu_id": null, "repositum_id": null, "title": "Statistics-Driven Localization of Dissimilarities in Data", "date": "2016-04", "abstract": "The identification of dissimilar regions in spatial and temporal data is a fundamental part of data exploration.\nThis process takes place in applications, such as biomedical image processing as well as climatic data analysis.\nWe propose a general solution for this task by employing well-founded statistical tools.\nFrom a large set of candidate regions, we derive an empirical distribution of the data and perform statistical hypothesis testing to obtain p-values as measures of dissimilarity.\nHaving p-values, we quantify differences and rank regions on a global scale according to their dissimilarity to user-specified exemplar regions.\nWe demonstrate our approach and its generality with two application scenarios, namely interactive exploration of climatic data and segmentation editing in the medical domain.\nIn both cases our data exploration protocol unifies the interactive data analysis, guiding the user towards regions with the most relevant dissimilarity characteristics.\nThe dissimilarity analysis results are conveyed with a radial tree, which prevents the user from searching exhaustively through all the data.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 700, "image_height": 700, "name": "karimov-2016-SD-image.png", "type": "image/png", "size": 173482, "path": "Publication:karimov-2016-SD", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-SD/karimov-2016-SD-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-SD/karimov-2016-SD-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 925, 869, 808, 166 ], "number": "TR-186-2-16-1", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 700, "image_height": 700, "name": "karimov-2016-SD-image.png", "type": "image/png", "size": 173482, "path": "Publication:karimov-2016-SD", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-SD/karimov-2016-SD-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-SD/karimov-2016-SD-image:thumb{{size}}.png" }, { "description": "Technical report", "filetitle": "technical report", "main_file": false, "use_in_gallery": false, "access": "public", "name": "karimov-2016-SD-technical report.pdf", "type": "application/csv", "size": 13697916, "path": "Publication:karimov-2016-SD", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-SD/karimov-2016-SD-technical report.pdf", "thumb_image_sizes": [] } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/karimov-2016-SD/", "__class": "Publication" }, { "id": "karimov-2015-HD", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Guided Volume Editing based on Histogram Dissimilarity", "date": "2015-05", "abstract": "Segmentation of volumetric data is an important part of many analysis pipelines, but frequently requires manual inspection and correction. While plenty of volume editing techniques exist, it remains cumbersome and error-prone for the user to find and select appropriate regions for editing. We propose an approach to improve volume editing by detecting potential segmentation defects while considering the underlying structure of the object of interest. Our method is based on a novel histogram dissimilarity measure between individual regions, derived from structural information extracted from the initial segmentation. Based on this information, our interactive system guides the user towards potential defects, provides integrated tools for their inspection, and automatically generates suggestions for their resolution. We demonstrate that our approach can reduce interaction effort and supports the user in a comprehensive investigation for high-quality segmentations.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "picture", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 869, "image_height": 726, "name": "karimov-2015-HD-picture.PNG", "type": "image/png", "size": 462098, "path": "Publication:karimov-2015-HD", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-picture.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-picture:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 925, 869, 808, 161 ], "date_from": "2015-05-25", "date_to": "2015-05-29", "event": "Eurographics Conference on Visualization (EuroVis)", "journal": "Computer Graphics Forum", "lecturer": [ 925 ], "location": "Cagliary, Italy", "number": "3", "pages_from": "91", "pages_to": "100", "volume": "34", "research_areas": [ "MedVis" ], "keywords": [ "Edge and feature detection", "Image Processing and Computer Vision", "Computer Graphics", "Display algorithms", "Picture/Image Generation", "Segmentation", "Methodology and Techniques", "Interaction techniques" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "karimov-2015-HD-paper.pdf", "type": "application/pdf", "size": 11473038, "path": "Publication:karimov-2015-HD", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "picture", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 869, "image_height": 726, "name": "karimov-2015-HD-picture.PNG", "type": "image/png", "size": 462098, "path": "Publication:karimov-2015-HD", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-picture.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-picture:thumb{{size}}.png" }, { "description": null, "filetitle": "presentation", "main_file": false, "use_in_gallery": false, "access": "public", "name": "karimov-2015-HD-presentation.pptx", "type": "application/zip", "size": 114199115, "path": "Publication:karimov-2015-HD", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-presentation.pptx", "thumb_image_sizes": [] }, { "description": null, "filetitle": "supplementary part 1", "main_file": false, "use_in_gallery": false, "access": "public", "name": "karimov-2015-HD-supplementary part 1.pdf", "type": "application/pdf", "size": 816263, "path": "Publication:karimov-2015-HD", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-supplementary part 1.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-supplementary part 1:thumb{{size}}.png" }, { "description": null, "filetitle": "supplementary part 2", "main_file": false, "use_in_gallery": false, "access": "public", "name": "karimov-2015-HD-supplementary part 2.pdf", "type": "application/pdf", "size": 20711943, "path": "Publication:karimov-2015-HD", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-supplementary part 2.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-supplementary part 2:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 822, "preview_image_height": 720, "name": "karimov-2015-HD-video.mp4", "type": "video/mp4", "size": 43392567, "path": "Publication:karimov-2015-HD", "preview_name": "karimov-2015-HD-video:preview.png", "preview_type": "image/jpeg", "preview_size": 41466, "url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/karimov-2015-HD-video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/", "__class": "Publication" }, { "id": "karimov-2013-vivisection", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "ViviSection: Skeleton-based Volume Editing", "date": "2013-06", "abstract": "Volume segmentation is important in many applications, particularly in the medical domain. Most segmentation\ntechniques, however, work fully automatically only in very restricted scenarios and cumbersome manual editing\nof the results is a common task. In this paper, we introduce a novel approach for the editing of segmentation\nresults. Our method exploits structural features of the segmented object to enable intuitive and robust correction\nand verification. 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