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IEEE International Conference on Big data

作者： Margaret Drouhard Chad A. Steed Steven Hahn Thomas Proffen Jamison Daniel Michael Matheson Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak Ridge TN National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN

In this paper, we propose strategies and objectives for immersive data visualization with applications in materials science using the Oculus Rift virtual reality headset. We provide background on currently available analysis tools for neutron scattering data and other large-scale materials science projects. In the context of the current challenges facing scientists, we discuss immersive virtual reality visualization as a potentially powerful solution. We introduce a prototype immersive visualization system, developed in conjunction with materials scientists at the Spallation Neutron Source, which we have used to explore large crystal structures and neutron scattering data. Finally, we offer our perspective on the greatest challenges that must be addressed to build effective and intuitive virtual reality analysis tools that will be useful for scientists in a wide range of fields.

关键词： data visualization Three-dimensional displays Neutrons Solid modeling Crystals Virtual reality Instruments

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Stabilization of Polar Nanoregions in Pb-free Ferroelectrics

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Physical Review Letters 2018年第20期120卷 207603-207603页

作者： A. Pramanick W. Dmowski T. Egami A. Setiadi Budisuharto F. Weyland N. Novak A. D. Christianson J. M. Borreguero D. L. Abernathy M. R. V. Jørgensen Department of Materials Science and Engineering City University of Hong Kong Kowloon Tong Hong Kong SAR Shull Wollan Center Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Materials Science and Engineering University of Tennessee Oak Ridge Tennessee 37831 USA Institute of Materials Science Technische Universität Darmstadt Darmstadt 64287 Germany Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Center for Materials Crystallography Department of Chemistry and iNANO Aarhus University 8000 Aarhus C Denmark MAX IV Laboratory Lund University SE-221 00 Lund Sweden

The formation of polar nanoregions through solid-solution additions is known to enhance significantly the functional properties of ferroelectric materials. Despite considerable progress in characterizing the microscopic behavior of polar nanoregions (PNR), understanding their real-space atomic structure and dynamics of their formation remains a considerable challenge. Here, using the method of dynamic pair distribution function, we provide direct insights into the role of solid-solution additions towards the stabilization of polar nanoregions in the Pb-free ferroelectric of Ba(Zr,Ti)O3. It is shown that for an optimum level of substitution of Ti by larger Zr ions, the dynamics of atomic displacements for ferroelectric polarization are slowed sufficiently below THz frequencies, which leads to increased local correlation among dipoles within PNRs. The dynamic pair distribution function technique demonstrates a unique capability to obtain insights into locally correlated atomic dynamics in disordered materials, including new Pb-free ferroelectrics, which is necessary to understand and control their functional properties.

关键词： Ferroelectricity Relaxor ferroelectrics Neutron pair-distribution function analysis

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Extreme data Management analysis and visualization:Exploiting Large data for Science Discovery

Extreme Data Management Analysis and Visualization:Exploitin...

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2014第三届国际新兴信息及通讯技术产业大会

作者： Valerio Pascucci Center for Extreme Data Management Analysis and Visualization (CEDMAV) University of Utah

Effective use of data management techniques for massive scientific data is a crucial ingredient for the success of any supercomputing center and data intensive scientific investigation. Developing such techniques involves a number of major challenges such as the real-time management of massive data, or the quantitative analysis of scientific features of unprecedented complexity. The Center for Extreme data Management analysis and visualization(CEDMAV) addresses these challenges with and interdisciplinary research in diverse topics including the mathematical foundations of data representations, the design of robust, efficient algorithms, and the integration with relevant applications in physics, biology, or medicine. In this talk, I will discuss one approach developed for dealing with massive amount of information via a framework for processing large scale scientific data with high performance selective queries on multiple terabytes of raw data. The combination of this data model with progressive streaming techniques allows achieving interactive processing rates on a variety of computing devices ranging from handheld devices like an i Phone, to simple workstations,to the I/O of parallel supercomputers. With this framework we demonstrated how one can enable the real time streaming of massive combustion simulations from DOE platforms such as Hopper2 at LBNL and Intrepid at ANL. I will also present the application of a discrete topological framework for the representation and analysis of the same large scale scientific data. Due to the combinatorial nature of this framework, we can implement the core constructs of Morse theory without the approximations and instabilities of classical numerical techniques. The inherent robustness of the combinatorial algorithms allows us to address the high complexity of the feature extraction problem for high resolution scientific data. During the talk, I will provide a live demonstration of the effectiveness of some software tools deve

关键词： Extreme data Management analysis and visualization:Exploiting Large data for Science Discovery

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Human Factors in Information visualization and Decision Support Systems

Human Factors in Information Visualization and Decision Supp...

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Mensch und Computer 2016, MuC 2016 - Workshopband - Human and Computer 2016, MuC 2016 - Workshop Proceedings

作者： Valdez, André Calero Brauner, Philipp Ziefle, Martina Kuhlen, Torsten Wolfgang Sedlmair, Michael Human-Computer Interaction Center RWTH Aachen University Germany Visual Computing Institute RWTH Aachen University Germany Visualization and Data Analysis Group TU Wien Austria

With the increase in data availability and data volume it becomes increasingly important to extract information and actionable knowledge from data. Information visualization helps the user to understand data by utilizing vision as a relatively parallel input channel to the user’s mind. Decision Support systems on the other hand help users in making information actionable, by suggesting beneficial decisions and presenting them in context. Both fields share a common need for understanding the interface between the computer and the human. This makes human factors research critical for both fields. Understanding limitations of human perception, cognition and action, as well as their variance must be understood to fully leverage information visualization and decision support. This article reflects on research agendas for investigating human factors in the aforementioned fields. Copyright © 2016 bei den Autoren.

关键词： Decision support systems

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Leaf glyphs: Story telling and data analysis using environmental data glyph metaphors

Communications in Computer and Information Science

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Communications in Computer and Information Science 2016年 598卷 123-143页

作者： Fuchs, Johannes Jäckle, Dominik Weiler, Niklas Schreck, Tobias Data Analysis and Visualization Group University of Konstanz Universitätstr. 10 Konstanz78457 Germany

In exploratory data analysis, important analysis tasks include the assessment of similarity of data points, labeling of outliers, identifying and relating groups in data, and more generally, the detection of patterns. Specifically, for large data sets, such tasks may be effectively addressed by glyph-based visualizations. Appropriately defined glyph designs and layouts may represent collections of data to address these aforementioned tasks. Important problems in glyph visualization include the design of compact glyph representations, and a similarityor structure-preserving 2D layout. Projection-based techniques are commonly used to generate layouts, but often suffer from over-plotting in 2D display space, which may hinder comparing and relating tasks. Inspired by contour and venation shapes of natural leafs, and their aggregation by stems, we introduce a novel glyph design for visualizing multi-dimensional data. Motivated by the human ability to visually discriminate natural shapes like trees in a forest, single flowers in a flowerbed, or leaves at shrubs, we design a flexible leaf-shaped data glyph, where data controls main leaf properties including leaf morphology, leaf venation, and leaf boundary shape. Our basic leaf glyph can map to more than a dozen of numeric and categorical variables. We also define custom visual aggregation schemes to scale the glyph for large numbers of data records, including prototype-based, set-based, and hierarchic aggregation. We show by example that our design is effectively interpretable to solve multivariate data analysis tasks, and provides effective data mapping. The design provides an aesthetically pleasing appearance, and lends itself easily to storytelling in environmental data analysis problems, among others. The glyph and its aggregation schemes are proposed as a scalable multivariate data visualization design, with applications in data visualization for mass media and data journalism, among others. © Springer International

关键词： visualization

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100 essential questions for the future of agriculture

Modern Agriculture

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Modern Agriculture 2023年第1期1卷

作者： Yuming Hu Taolan Zhao Yafang Guo Meng Wang Kerstin Brachhold Chengcai Chu Andrew Hanson Sachin Kumar Rongcheng Lin Wenjin Long Ming Luo Jian Feng Ma Yansong Miao Shaoping Nie Yu Sheng Weiming Shi James Whelan Qingyu Wu Ziping Wu Wei Xie Yinong Yang Chao Zhao Lei Lei Yong-Guan Zhu Qifa Zhang Wiley-VCH GmbH Weinheim Germany Contribution: Conceptualization (equal) Data curation (equal) Formal analysis (equal) Investigation (equal) Methodology (equal) Project administration (equal) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Investigation (equal) Resources (equal) Writing - original draft (equal) Contribution: Data curation (equal) Investigation (equal) Visualization (equal) Contribution: Writing - review & editing (equal) State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources College of Agriculture South China Agricultural University Guangzhou China Contribution: Investigation (equal) Resources (equal) Horticultural Sciences Department University of Florida Gainesville Florida USA Rumen Biotechnology Laboratory Animal Nutrition Division ICAR-National Dairy Research Institute Karnal Haryana India Key Laboratory of Photobiology Institute of Botany Chinese Academy of Sciences Beijing China School of Economics and Management China Agricultural University Beijing China Guangdong Provincial Key Laboratory of Applied Botany & Agriculture and Biotechnology Research Center South China Botanical Garden Chinese Academy of Sciences Guangzhou China Institute of Plant Science and Resources Okayama University Kurashiki Japan School of Biological Sciences Nanyang Technological University Singapore City Singapore State Key Laboratory of Food Science and Technology China-Canada Joint Lab of Food Science and Technology (Nanchang) Nanchang University Nanchang China China Center for Agricultural Policy School of Advanced Agricultural Sciences Peking University Beijing China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing China State Key Laboratory of Plant Physiology and Biochemistry College of Life Sciences Zhejiang University Hangzhou Zhejiang China Department of Animal Plant and Soil Sciences La Trobe University Me

The world is at a crossroad when it comes to agriculture. The global population is growing, and the demand for food is increasing, putting a strain on our agricultural resources and practices. To address this challenge, innovative, sustainable, and inclusive approaches to agriculture are urgently required. In this paper, we launched a call for Essential Questions for the Future of Agriculture and identified a priority list of 100 questions. We focus on 10 primary themes: transforming agri-food systems, enhancing resilience of agriculture to climate change, mitigating climate change through agriculture, exploring resources and technologies for breeding, advancing cultivation methods, sustaining healthy agroecosystems, enabling smart and controlled-environment agriculture for food security, promoting health and nutrition-driven agriculture, exploring economic opportunities and addressing social challenges, and integrating one health and modern agriculture. We emphasise the critical importance of interdisciplinary and multidisciplinary research that integrates both basic and applied sciences and bridges the gaps among various stakeholders for achieving sustainable agriculture.

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analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Self organizing maps for the visual analysis of pitch contours 20

Self organizing maps for the visual analysis of pitch contou...

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20th Nordic Conference of Computational Linguistics, NODALIDA 2015

作者： Sacha, Dominik Asano, Yuki Rohrdantz, Christian Hamborg, Felix Keim, Daniel Braun, Bettina Butt, Miriam Data Analysis and Visualization Group Department of Linguistics University of Konstanz Germany

ISBN: (纸本)9789175190983

We present a novel interactive approach for the visual analysis of intonation contours. Audio data are processed algorithmically and presented to researchers through interactive visualizations. To this end, we automatically analyze the data using machine learning in order to find groups or patterns. These results are visualized with respect to meta-data. We present a flexible, interactive system for the analysis of prosodic data. Using realworld application examples, one containing preprocessed, the other raw data, we demonstrate that our system enables researchers to interact dynamically with the data at several levels and by means of different types of visualizations, thus arriving at a better understanding of the data via a cycle of hypothesis generation and testing that takes full advantage of our visual processing abilities. © NODALIDA *** right reserved.

关键词： Self organizing maps

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Efficient dense reconstruction using geometry and image consistency constraints

Efficient dense reconstruction using geometry and image cons...

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IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015

作者： Shashkov, Mikhail M. Mak, Jason Recker, Shawn Nguyen, Connie Owens, John Joy, Kenneth I. Institute for Data Analysis and Visualization University of California - Davis DavisCA95616 United States

ISBN: (纸本)9781467395588

We introduce a method for creating very dense reconstructions of datasets, particularly turn-table varieties. The method takes in initial reconstructions (of any origin) and makes them denser by interpolating depth values in two-dimensional image space within a superpixel region and then optimizing the interpolated value via image consistency analysis across neighboring images in the dataset. One of the core assumptions in this method is that depth values per pixel will vary gradually along a gradient for a given object. As such, turntable datasets, such as the dinosaur dataset, are particularly easy for our method. Our method modernizes some existing techniques and parallelizes them on a GPU, which produces results faster than other densification methods. © 2015 IEEE.

关键词： Phonographs

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