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Joint 1st International Workshop on Knowledge Requirements: Acquisition and Representation, KRAR 2016 and the 1st Regional Consortium for Computing Sciences and Foundation, RCCS 2016

作者： Pollo-Cattaneo, M.F. Pesado, P. Britos, P. García-Martínez, R. PhD Program on Computer Science Computer Science School National University of La Plata Information System Methodologies Research Group Technological National University Buenos Aires Argentina School of Computer Science National University of La Plata Buenos Aires Argentina Information Mining Research Group National. Lab. ID + I in Visualization Computer Graphics and Creative Code University of Rio Negro at El Bolsón Río Negro Argentina Information Systems Research Group National University of Lanus Buenos Aires Argentina

There can be no debate that Information Mining Projects cause processing tools to arise with the sole purpose of converting available organization data into useful knowledge on account of decision-making. Considering the aforementioned, this type of projects demands due diligence in the requirement specification process, as the latter needs to be thorough and traceable throughout the entire project, and therefore, process associated with requirements engineering are not to be reused in any future projects among the like. Similarly, latest methodologies within the field of Information Mining fail to take practices associated to stakeholders and costumers' requirements management into account. With this aim in mind, a solution model to the Information Mining Project Management necessities is proposed. © 2016, CEUR-WS. All rights reserved.

关键词： Requirements engineering

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Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation

TechRxiv

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TechRxiv 2023年

作者： Mody, Prerak de Plaza, Nicolas Chaves Astrenidou, Eleftheria de Ridder, Mischa Hoekstra, Nienke Hildebrandt, Klaus Staring, Marius Radiology Leiden University Medical Center Netherlands The Department of Radiation Oncology Leiden University Medical Center Netherlands University Medical Center Utrecht Netherlands The Computer Graphics and Visualization Lab TU Delft Netherlands

Increased usage of automated tools like deep learning in medical image segmentation has alleviated the bottleneck of manual contouring. This has shifted manual labour to quality assessment (QA) of automated contours which involves detecting errors and correcting them. A potential solution to semi-automated QA is to use deep Bayesian uncertainty to recommend potentially erroneous regions, thus reducing time spent on error detection. Previous work has investigated the correspondence between uncertainty and error, however, no work has been done on improving the "utility" of Bayesian uncertainty maps such that it is only present in inaccurate regions and not in the accurate ones. Our work trains the FlipOut model with the Accuracy-vs-Uncertainty (AvU) loss which promotes uncertainty to be present only in inaccurate regions. We apply this method on datasets of two radiotherapy body sites, namely CT scans of head and neck and prostate MRs. Uncertainty heatmaps (i.e. predictive entropy) are evaluated using ROC and Precision-Recall curves. Numerical results show that when compared to the Bayesian baseline the proposed method successfully suppresses uncertainty for accurate voxels, with similar presence of uncertainty for inaccurate voxels. Code to reproduce experiments is available on https://***/prerakmody/ bayesuncertainty-error-correspondence. © 2023, CC BY.

关键词： computerized tomography

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SHREC'19 track: Extended 2D scene image-based 3D scene retrieval 12

SHREC'19 track: Extended 2D scene image-based 3D scene retri...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer graphics, EG 2019

作者： Abdul-Rashid, Hameed Yuan, Juefei Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Holenderski, Mike Jarnikov, Dmitri Le, Khiem T. Menkovski, Vlado Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Ninh, Tu V. Rey, Perez Tran, Minh-Triet Wang, Tianyang School of Computer Science and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institue of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

In the months following our SHREC 2018 - 2D Scene Image-Based 3D Scene Retrieval (SceneIBR2018) track, we have extended the number of the scene categories from the initial 10 classes in the SceneIBR2018 benchmark to 30 classes, resulting in a new benchmark SceneIBR2019 which has 30,000 scene images and 3,000 3D scene models. For that reason, we seek to further evaluate the performance of existing and new 2D scene image-based 3D scene retrieval algorithms using this extended and more comprehensive new benchmark. Three groups from the Netherlands, The United States and Vietnam participated and collectively submitted eight runs. This report documents the evaluation of each method based on seven performance metrics, offers an in-depth discussion as well as analysis on the methods employed and discusses future directions that have the potential to address this task. Again, deep learning techniques have demonstrated notable performance in terms of both accuracy and scalability when applied to this exigent retrieval task. To further enrich the current state of 3D scene understanding and retrieval, our evaluation toolkit, all participating methods' results and the comprehensive 2D/3D benchmark have all been made publicly available. © 2019 The Author(s)

关键词： Three dimensional computer graphics

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SHREC'19 track: Extended 2D scene sketch-based 3D scene retrieval 12

SHREC'19 track: Extended 2D scene sketch-based 3D scene retr...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer graphics, EG 2019

作者： Yuan, Juefei Abdul-Rashid, Hameed Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Tran, Minh-Triet Wang, Tianyang School of Computing Sciences and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institute of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications. © 2019 The Author(s)

关键词： Scalability

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PREVIS - A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control

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Procedia CIRP 2022年 112卷 483-488页

作者： Patrick Ruediger Felix Claus Viktor Leonhardt Hans Hagen Jan C. Aurich Christoph Garth Institute for Manufacturing Technolgy and Production Systems (FBK) Technische Universität Kaiserslautern P. O. Box 3049 67653 Kaiserslautern Germany Computer Graphics & HCI Lab Technische Universität Kaiserslautern P. O. Box 3049 67653 Kaiserslautern Germany Scientific Visualization Lab Technische Universität Kaiserslautern P. O. Box 3049 67653 Kaiserslautern Germany

We present PREVIS, a visual analytics tool, enhancing machine learning performance analysis in engineering applications. The presented toolchain allows for a direct comparison of regression models. In addition, we provide a methodology to visualize the impact of regression errors on the underlying field of interest in the original domain, the part geometry, via exploiting standard interpolation methods. Further, we allow a real-time preview of user-driven parameter changes in the displacement field via visual interpolation. This allows for fast and accountable online change management. We demonstrate the effectiveness with an ex-ante optimization of an automotive engine hood.

关键词： Explainable Machine Learning Quality Control Reverse Engineering Assembly Process visualization

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FluidPlaying: Efficient Adaptive Simulation for Highly Dynamic Fluid

FluidPlaying: Efficient Adaptive Simulation for Highly Dynam...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Sinuo Liu Xiaojuan Ban Sheng Li Haokai Zeng Xiaokun Wang Yanrui Xu Fei Zhu Guoping Wang Graphics & Interaction Lab National Biomedical Imaging Center School of Computer Science Peking University School of Intelligence Science and Technology University of Science and Technology Beijing Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

We present FliudPlaying, a novel dynamic level-based spatially adaptive simulation method that can handle highly dynamic fluid efficiently. To capture the subtle detail of the fluid surface, the high-resolution simulation is performed not only at the free surface but also at those regions with high vorticity levels and velocity difference levels. To minimize the density error, an online optimization scheme is used when increasing the resolution by particle splitting. We also proposed a neighbor-based splash enhancement to compensate for the loss of dynamic details. Compared with the high-resolution simulation baseline, our method can achieve over 3× speedups while consuming only less than 10% computational resources. Furthermore, our method can make up for the loss of high-frequency details caused by the spatial adaptation, and provide more realistic dynamics in particle-based fluid simulation.

关键词： Solid modeling Adaptation models Three-dimensional displays Fluid dynamics Computational modeling Conferences Virtual reality

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OMiCroN - Oblique Multipass Hierarchy Creation while Navigating

arXiv

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arXiv 2020年

作者： da Silva, Vinícius Esperança, Claudio Marroquim, Ricardo Computer Graphics and Visualization Group Van Mourik Broekmanweg 6 Delft Netherlands VISGRAF Lab Estrada Dona Castorina 110 Jardim Botânico Rio de Janeiro - RJCEP: 22460-320 Brazil Cidade Universitária Centro de Tecnologia Block H Rio de Janeiro - RJ21941-972 Brazil

Rendering large point clouds ordinarily requires building a hierarchical data structure for accessing the points that best represent the object for a given viewing frustum and level-of-detail. The building of such data structures frequently represents a large portion of the cost of the rendering pipeline both in terms of time and space complexity, especially when rendering is done for inspection purposes only. This problem has been addressed in the past by incremental construction approaches, but these either result in low quality hierarchies or in longer construction times. In this work we present OMiCroN – Oblique Multipass Hierarchy Creation while Navigating – which is the first algorithm capable of immediately displaying partial renders of the geometry, provided the cloud is made available sorted in Morton order. OMiCroN is fast, being capable of building the entire data structure in memory spending an amount of time that is comparable to that of just reading the cloud from disk. Thus, there is no need for storing an expensive hierarchy, nor for delaying the rendering until the whole hierarchy is read from disk. In fact, a pipeline coupling OMiCroN with an incremental sorting algorithm running in parallel can start rendering as soon as the first sorted prefix is produced, making this setup very convenient for streamed *** Codes [2010]: 68U05 Copyright © 2020, The Authors. All rights reserved.

关键词： Data structures

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PREVIS-A combined machine learning and visual interpolation approach for interactive reverse engineering in assembly quality control

arXiv

引用

arXiv 2022年

作者： Ruediger, Patrick Claus, Felix Leonhardt, Viktor Hagen, Hans Aurich, Jan C. Garth, Christoph Technische Universität Kaiserslautern P. O. Box 3049 Kaiserslautern67653 Germany Computer Graphics & HCI Lab Technische Universität Kaiserslautern P. O. Box 3049 Kaiserslautern67653 Germany Scientific Visualization Lab Technische Universität Kaiserslautern P. O. Box 3049 Kaiserslautern67653 Germany

关键词： Quality control

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Nuwa: An Authoring Tool for Graph visualizations

Nuwa: An Authoring Tool for Graph Visualizations

引用

Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Dongming Han Haiyang Zhu Wei Chen Jiacheng Pan Ran Chen Xumeng Wang Zhen Wen Luoxuan Weng Minfeng Zhu Yingcai Wu Rüdiger Westermann State Key Lab of CAD&CG Zhejiang University China Hithink RoyalFlush Information Network Co. Ltd. China Wuchan Zhongda Digital Technology Co. Ltd. China Laboratory of Art and Archaeology Image (Zhejiang University) Ministry of Education China College of Computer Science Nankai University China Zhejiang University China Computer Graphics and Visualization Technical University of Munich Germany

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

Authoring graph visualization requires advanced programming skills, expert domain knowledge, and significant workload. Existing authoring tools either support limited templates of graph visualization, or suffer from a high learning cost. We analyze the design requirements on a tool for graph visualizations, and contribute Nuwa, a user-friendly declarative authoring tool for the interactive specification of graph visualizations in terms of data, entity, change, and encoding. Our implementation empowers users to conveniently create, compare, and modulate comprehensive graph visualizations with a wide range of styles. We showcase various examples to verify the expressiveness of Nuwa. Via an expert interview and the analysis on cognitive dimensions we evaluate the usability of Nuwa.

关键词： Analytical models Authoring systems Costs Semantics Data visualization Prototypes Programming

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Highlights from the 5th Symposium on Biological Data visualization: Part 2

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BMC proceedings 2015年第SUPPL 6 PROCEEDINGS OF THE 5TH SYMPOSIUM ON BIOLOGICAL DATA期9卷 S1页

作者： Jan Aerts G Elisabeta Marai Kay Nieselt Cydney Nielsen Marc Streit Daniel Weiskopf Visual Data Analysis Lab ESAT/STADIUS KU Leuven Kasteelpark Arenberg 10 3001 Leuven Belgium iMinds Medical IT KU Leuven Kasteelpark Arenberg 10 3001 Leuven Belgium. Electronic Visualization Lab University of Illinois at Chicago 851 S. Morgan St Chicago IL 60607 USA. Center for Bioinformatics University of Tübingen Sand 14 72076 Tübingen Germany. Department of Pathology and Laboratory Medicine University of British Columbia and British Columbia Cancer Agency 675 West 10th Avenue Vancouver British Columbia V5Z 1L3 Canada. Institute of Computer Graphics Johannes Kepler University Linz Altenberger Straße 69 4020 Linz Austria. VISUS University of Stuttgart Allmandring 19 70569 Stuttgart Germany.

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