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作者： Jang, Justin Wonka, Peter Ribarsky, William Shaw, Christopher D. Georgia Institute of Technology GVU Center College of Computing Atlanta GA 30332 United States Arizona State University PRISM Dept. of Computer Science and Engineering Tempe AZ 85287 United States UNC Charlotte CS Dept. Charlotte Visualization Center Charlotte NC 28223 United States Simon Fraser University Surrey School of Interactive Arts and Technology 14-660 Central City Tower 13450 102 Ave Surrey BC V3T 5X3 Canada Graphics Visualization and Usability Center Georgia Institute of Technology PRISM Lab. ASU Department of Information Technology UNC Charlotte Charlotte Visualization Center IEEE Visualization and Graphics Technical Committee School of Interactive Arts and Technology Simon Fraser University Surrey

We present a simplification algorithm for manifold polygonal meshes of plane-dominant models. Models of this type are likely to appear in man-made environments. While traditional simplification algorithms focus on generality and smooth meshes, the approach presented here considers a specific class of man-made models. By detecting and classifying edge loops on the mesh and providing a guided series of binary mesh partitions, our approach generates a series of simplified models, each of which better respects the semantics of these kinds of models than conventional approaches do. A guiding principle is to eliminate simplifications that do not make sense in constructed environments. This, coupled with the concept of "punctuated simplification", leads to an approach that is both efficient and delivers high visual quality. Comparative results are given.

关键词： Object oriented programming

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GUEST EDITOR'S FOREWORD

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International Journal of Computational Geometry & Applications 1999年第4N05期9卷 325-325页

作者： PANKAJ K. AGARWAL Center for Geometric Computing Department of Computer Science Box 90129 Duke University Durham NC 27708-0129 USA

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A method for automatic forensic facial reconstruction based on dense statistics of soft tissue thickness

arXiv

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arXiv 2018年

作者： Gietzen, Thomas Brylka, Robert Achenbach, Jascha Hebel, Katjazum Schömer, Elmar Botsch, Mario Schwanecke, Ulrich Schulze, Ralf RheinMain University of Applied Sciences Computing for New Media Wiesbaden Germany Bielefeld University Computer Graphics Group Bielefeld Germany University Medical Center of the Johannes Gutenberg University Mainz Department of Prosthetic Dentistry Mainz Germany Johannes Gutenberg University Mainz Institute of Computer Science Mainz Germany University Medical Center Johannes Gutenberg University Mainz Section of Oral Radiology Mainz Germany

In this paper, we present a method for automated estimation of a human face given a skull remain. The proposed method is based on three statistical models. A volumetric (tetrahedral) skull model encoding the variations of different skulls, a surface head model encoding the head variations, and a dense statistic of facial soft tissue thickness (FSTT). All data are automatically derived from computed tomography (CT) head scans and optical face scans. In order to obtain a proper dense FSTT statistic, we register a skull model to each skull extracted from a CT scan and determine the FSTT value for each vertex of the skull model towards the associated extracted skin surface. The FSTT values at predefined landmarks from our statistic are well in agreement with data from the literature. To recover a face from a skull remain, we first fit our skull model to the given skull. Next, we generate spheres with radius of the respective FSTT value obtained from our statistic at each vertex of the registered skull. Finally, we fit a head model to the union of all spheres. The proposed automated method enables a probabilistic face-estimation that facilitates forensic recovery even from incomplete skull remains. The FSTT statistic allows the generation of plausible head variants, which can be adjusted intuitively using principal component analysis. We validate our face recovery process using an anonymized head CT scan. The estimation generated from the given skull visually compares well with the skin surface extracted from the CT scan itself. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistics

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The discrete 2-center problem 97

The discrete 2-center problem

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Proceedings of the thirteenth annual symposium on Computational geometry

作者： Pankaj K. Agarwal Micha Sharir Emo Welzl Center for Geometric Computing Department of Computer Science Box 90129 Duke University Durham NC School of Mathematical Sciences Tel Aviv University Tel Aviv 69978 Israel and Courant Institute of Mathematical Sciences New York University New York NY Institut für Theoretische Informatik ETH Zürich CH-8092 Zürich Switzerland

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Foreword

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Algorithmica 2000年第1期26卷 1-2页

作者： T. Nishizeki R. Tamassia D. Wagner Graduate School of Information Sciences Tohoku University Sendai 480-77 Japan. nishi@ecei.tohoku.ac.jp. JP Center for Geometric Computing Department of Computer Science Brown University Providence RI 02912-1910 USA. rt@cs.brown.edu. US Fakultät für Mathematik and Informatik Universität Konstanz Fach D188 D-78457 Konstanz Germany. Dorothea.Wagner@Uni-Konstanz.de. DE

No Abstract.

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 1997年 1272卷

作者： Dehne, Frank Rau-Chaplin, Andrew Sack, Jörg-Rüdiger Tamassia, Roberto Carleton University School of Computer Science 1125 Colonel By Drive Ottawa Canada Technical University of Nova Scotia Department of Computer Science RO. Box 1000 HalifaxNS Canada Brown University Center for Geometric Computing ProvidenceRI United States

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Development of Software for the Implementation of an Adaptive Approach to the Generation of Internal Filling of a 3D Model in Additive Manufacturing

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Journal of Physics: Conference Series 2021年第1期2096卷

作者： A A Kholodilov E V Faleeva M V Kholodilova Department of "Computer Science and Computer Graphics" Far Eastern State Transport University Serysheva st. 47 Khabarovsk 680021 Russia Computing Center of the Far Eastern Branch Russian Academy of Sciences Kim Yoo Jong st. 65 Khabarovsk 680000 Russia

The article describes the technology of software development for the implementation of an adaptive approach to generating the external and internal structure of a 3D model in preparation for 3D printing when transferring from *.stl format to * *** format. The main stages of software design, the principles of mathematical modeling underlying the development are considered, the practical significance of the developed software complex-slicer is shown with a description of the functionality and conceptual development.

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Investigation of the Mathematical and Software Implementation of Triangulation Mesh Generation Algorithms in Relation to the Creation of a Slicer Program

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Journal of Physics: Conference Series 2021年第1期2096卷

作者： A A Kholodilov E V Faleeva M V Kholodilova Department of "Computer Science and Computer Graphics" Far Eastern State Transport University st. Serysheva 47 Khabarovsk 680021 Russia Computing Center of the Far Eastern Branch Russian Academy of Sciences st. Kim Yoo Jong 65 Khabarovsk 680000 Russia

The article discusses an algorithmic method for increasing the strength characteristics of products produced by additive manufacturing technologies through thickening the internal grid structure of filling the model when transferring to the control code during the operation of the slicer program. It shows the internal filling using the Voronoi grid and the "Exhaustion Algorithm" for the implementation of the internal filling of the threedimensional model in the process of the slicer program.

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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

arXiv

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arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image computing and computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： computerized tomography

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