[ { "id": "amirkhanov-2021-diss", "type_id": "phdthesis", "tu_id": 301689, "repositum_id": "20.500.12708/19584", "title": "Visual Analysis of Defects", "date": "2021", "abstract": "In everyday life, we use many objects on which we rely and expect them to work correctly. We use phones to communicate with friends, bicycles to commute, payment cards to buy groceries. However, due to defects, these objects may fail at some time, leading to adverse outcomes. Modern industry continually improves the quality of outputs (e.g., products and services) and ensures that they meet their specifications. A common quality management strategy is the defect analysis used to identify and control outputs that do not conform to their specifications. Traditional defect analysis methods are often manual and, therefore, time-consuming procedures. To build more efficient solutions, defect analysis increasingly employs visual analytics techniques. These techniques automatize and enhance the up-to-now manual analysis steps and support new visual approaches for defect representations that resolve existing defects without introducing new ones. In this dissertation, visual analytics techniques applied to defect analysis are referred to as visual analysis of defects. Being a rapidly developing area, the domain of visual analysis of defects is still missing a formalized basis.\n\nThis dissertation presents and discusses a workflow for the visual analysis of defects based on the plan-do-check-act cycle of continual improvement. The workflow consists of four steps: defect prevention, control of defective outputs, performance evaluation, and improvement. During the defect prevention step, domain experts plan the design and development processes to ensure that intended results can be achieved while forecasting risks and opportunities. During the control of defective outputs step, domain experts implement the processes and control defects arising throughout these processes. During the performance evaluation step, domain experts ensure that defective outputs are identified by measuring the object's characteristics. During the improvement step, domain experts explore possible actions that improve the object quality.\n\nThis dissertation presents four solutions that advance the visual analysis of defects at the four distinct steps of the workflow. The first solution corresponds to the defect prevention step and provides a preview of dental treatment. It helps dental technicians to identify the most suitable treatment option and avoid cases when patients are unsatisfied with the results due to poor denture aesthetics. The second solution corresponds to the control of defective outputs step and supports dental technicians in designing aesthetic and functional dentures. The approach provides immediate visual feedback on a change in the denture design, which helps to evaluate how the change affects aesthetics. The third solution corresponds to the performance evaluation step and supports material engineers in investigating the damage mechanism in composite materials. First, the system captures and measures various defects such as matrix fracture, fiber/matrix debonding, fiber pull-out, and fiber fracture. Later, users analyze these defects using several interactive visualization techniques. The fourth solution corresponds to the improvement step and visualizes 4D dynamical systems describing various phenomena. The solution enables the 4D representation of dynamical systems and allows the 4D representation to seamlessly transition into, familiar to the user, lower-dimensional plots.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1731, "image_height": 1726, "name": "amirkhanov-2021-diss-teaser.jpg", "type": "image/jpeg", "size": 119695, "path": "Publication:amirkhanov-2021-diss", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/amirkhanov-2021-diss/amirkhanov-2021-diss-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/amirkhanov-2021-diss/amirkhanov-2021-diss-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 1170 ], "co_supervisor": [ 869, 611 ], "date_end": "2021", "date_start": "2014", "doi": "10.34726/hss.2022.99023", "open_access": "yes", "pages": "178", "reviewer_1": [ 1711 ], "reviewer_2": [ 563 ], "rigorosum": "2021-10-18", "supervisor": [ 166 ], "research_areas": [ "MedVis", "VisMat" ], "keywords": [ "visualization", "visual analytics", "defect analysis", "dentistry", "material science", "mathematical visualization" ], "weblinks": [], "files": [ { "description": null, "filetitle": "Dissertation", "main_file": false, "use_in_gallery": false, "access": "public", "name": "amirkhanov-2021-diss-Dissertation.pdf", "type": "application/pdf", "size": 12790468, "path": "Publication:amirkhanov-2021-diss", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/amirkhanov-2021-diss/amirkhanov-2021-diss-Dissertation.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/amirkhanov-2021-diss/amirkhanov-2021-diss-Dissertation:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1731, "image_height": 1726, "name": "amirkhanov-2021-diss-teaser.jpg", "type": "image/jpeg", "size": 119695, "path": "Publication:amirkhanov-2021-diss", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/amirkhanov-2021-diss/amirkhanov-2021-diss-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/amirkhanov-2021-diss/amirkhanov-2021-diss-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "Smile Analytics" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/amirkhanov-2021-diss/", "__class": "Publication" }, { "id": "amirkhanov-2018-withteeth", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "WithTeeth: Denture Preview in Augmented Reality", "date": "2018-10", "abstract": "Dentures are prosthetic devices replacing missing or damaged teeth, often used for dental reconstruction. Dental reconstruction improves the functional state and aesthetic appearance of teeth. State-of-the-art methods used by dental technicians typically do not include the aesthetic analysis, which often leads to unsatisfactory results for patients. In this paper, we present a virtual mirror approach for a dental treatment preview in augmented reality. Different denture presets are visually evaluated and compared by switching them on the fly. Our main goals are to provide a virtual dental treatment preview to facilitate early feedback, and hence to build the confidence and trust of patients in the outcome. The workflow of our algorithm is as follows. First, the face is detected and 2D facial landmarks are extracted. Then, 3D pose estimation of upper and lower jaws is performed and high-quality 3D models of the upper and lower dentures are fitted. The fitting uses the occlusal plane angle as determined manually by dental technicians. To provide a realistic impression of the virtual teeth, the dentures are rendered with motion blur. We demonstrate the robustness and visual quality of our approach by comparing the results of a webcam to a DSLR camera under natural, as well as controlled lighting conditions.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1170, 817, 660, 582, 1628, 1629, 166 ], "address": "https://diglib.eg.org/handle/10.2312/vmv20181250", "booktitle": "Vision, Modeling and Visualization", "cfp": { "name": "call_for_paper_vmv18.pdf", "type": "application/pdf", "error": "0", "size": "748387", "orig_name": "call_for_paper_vmv18.pdf", "ext": "pdf" }, "editor": "Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip", "event": "VMV18", "isbn": "978-3-03868-072-7", "lecturer": [ 1170 ], "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "amirkhanov-2018-withteeth-paper.pdf", "type": "application/pdf", "size": 4951324, "path": "Publication:amirkhanov-2018-withteeth", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/amirkhanov-2018-withteeth/amirkhanov-2018-withteeth-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/amirkhanov-2018-withteeth/amirkhanov-2018-withteeth-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "video demo", "main_file": true, "use_in_gallery": false, "access": "public", "name": "amirkhanov-2018-withteeth-video demo.mp4", "type": "video/mp4", "size": 35459157, "path": "Publication:amirkhanov-2018-withteeth", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/amirkhanov-2018-withteeth/amirkhanov-2018-withteeth-video demo.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/amirkhanov-2018-withteeth/amirkhanov-2018-withteeth-video demo:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2018/amirkhanov-2018-withteeth/amirkhanov-2018-withteeth-video demo:video.mp4" } ], "projects_workgroups": [ "Smile Analytics" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/amirkhanov-2018-withteeth/", "__class": "Publication" } ]