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arXiv 2020年

作者： Das, Priyanka McGrath, Joseph Fang, Zhaoyuan Boyd, Aidan Jang, Ganghee Mohammadi, Amir Purnapatra, Sandip Yambay, David Marcel, Sbastien Trokielewicz, Mateusz Maciejewicz, Piotr Bowyer, Kevin Czajka, Adam Schuckers, Stephanie Tapia, Juan Gonzalez, Sebastian Fang, Meiling Damer, Naser Boutros, Fadi Kuijper, Arjan Sharma, Renu Chen, Cunjian Ross, Arun Clarkson University United States University of Notre Dame United States Warsaw University of Technology Poland Idiap Research Institute Switzerland Medical University of Warsaw Poland Universidad de Santiago Chile Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science Technical University of Darmstadt Darmstadt Germany TOC Biometrics Chile Michigan State University United States

Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results from the fourth competition of the series: LivDet-Iris 2020. This year’s competition introduced several novel elements: (a) incorporated new types of attacks (samples displayed on a screen, cadaver eyes and prosthetic eyes), (b) initiated LivDet-Iris as an on-going effort, with a testing protocol available now to everyone via the Biometrics Evaluation and Testing (BEAT)* open-source platform to facilitate reproducibility and benchmarking of new algorithms continuously, and (c) performance comparison of the submitted entries with three baseline methods (offered by the University of Notre Dame and Michigan State University), and three open-source iris PAD methods available in the public domain. The best performing entry to the competition reported a weighted average APCER of 59.10% and a BPCER of 0.46% over all five attack types. This paper serves as the latest evaluation of iris PAD on a large spectrum of presentation attack instruments. Copyright © 2020, The Authors. All rights reserved.

关键词： Competition

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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Single trial ERP amplitudes reveal the time course of acquiring representations of novel faces in individual participants

arXiv

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arXiv 2020年

作者： Sommer, Werner Stapor, Katarzyna Kończak, Grzegorz Kotowski, Krzysztof Fabian, Piotr Ochab, Jeremi Bereś, Anna Ślusarczyk, Grażyna Humboldt-University at Berlin Department of Psychology Germany Silesian University of Technology Institute of Computer Science Gliwice Poland University of Economics in Katowice Department of Statistics Econometrics and Mathematics Poland Silesian University of Technology Department of Applied Informatics Gliwice Poland Silesian University of Technology Department of Algorithmics and Software Gliwice Poland Jagiellonian University Department of Theory of Complex Systems Krakow Poland Jagiellonian University Department of Cognitive Neuroscience and Neuroergonomics Krakow Poland Jagiellonian University Department of Design and Computer Graphics Krakow Poland Institut für Psychologie Humboldt-Universität zu Berlin Rudower Chaussee 18 Berlin12489 Germany Faculty of Automatic Control Electronics and Computer Science Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

The neural correlates of face individuation – the acquisition of memory representations for novel faces – have been studied only in coarse detail and disregarding individual differences between learners. In their seminal study, (Tanaka, Curran, Porterfield, & Collins, 2006) required the identification of a particular novel face across 70 trials and found that the N250 component in the ERP became more negative from the first to the second half of the experiment, where it reached a similar amplitude as a well-known face. We were unable to directly replicate this finding in our study when we used the original spilt of trials. However, when we applied a different split of trials we observed very similar changes in N250 amplitude. Then, we developed and applied a new two-step explorative-confirmative non-parametric method based on permutation testing to determine the time course of face individuation in individual participants based on single trial N250 amplitudes. We show that the assumption of a steep initial increase of N250 amplitude across multiple presentations of the target face, followed by a plateau, yields plausible results in fitting linear trends for most participants. The transition point from initial acquisition to the plateau phase differed strongly between participants and tended to be earlier when performance in target face recognition was better. Hence, face individuation may be accounted for by a biphasic process of early, fast acquisition, followed by a slower, asymptotic consolidation or maintenance phase. The current approach might be fruitfully applied to further investigations into face individuation and their neural correlates. © 2020, CC BY-NC-ND.

关键词： Face recognition

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Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data

arXiv

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arXiv 2019年

作者： Gsaxner, Christina Roth, Peter M. Wallner, Jürgen Egger, Jan Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz Styria Austria

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Revealing the Invisible: Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis

Revealing the Invisible: Visual Analytics and Explanatory St...

引用

2018 International Symposium on Big Data Visual and Immersive Analytics, BDVA 2018

作者： Stein, Manuel Breitkreutz, Thorsten Haussler, Johannes Seebacher, Daniel Niederberger, Christoph Schreck, Tobias Grossniklaus, Michael Keim, Daniel Janetzko, Halldor Department of Computer and Information Science University of Konstanz Germany VIZRT Switzerland Institute for Computer Graphics and Knowledge Visualization Graz University of Technology Austria Department of Geography University of Zurich Switzerland

ISBN: (纸本)9781538691946

The analysis of invasive team sports often concentrates on cooperative and competitive aspects of collective movement behavior. A main goal is the identification and explanation of strategies, and eventually the development of new strategies. In visual sports analytics, a range of different visual-interactive analysis techniques have been proposed, e.g., based on visualization using for example trajectories, graphs, heatmaps, and animations. Identifying suitable visualizations for a specific situation is key to a successful analysis. Existing systems enable the interactive selection of different visualization facets to support the analysis process. However, an interactive selection of appropriate visualizations is a difficult, complex, and time-consuming task. In this paper, we propose a four-step analytics conceptual workflow for an automatic selection of appropriate views for key situations in soccer games. Our concept covers classification, specification, explanation, and alteration of match situations, effectively enabling the analysts to focus on important game situations and the determination of alternative moves. Combining abstract visualizations with real world video recordings by Immersive Visual Analytics and descriptive storylines, we support domain experts in understanding key situations. We demonstrate the usefulness of our proposed conceptual workflow via two proofs of concept and evaluate our system by comparing our results to manual video annotations by domain experts. Initial expert feedback shows that our proposed concept improves the understanding of competitive sports and leads to a more efficient data analysis. © 2018 IEEE.

关键词： Visualization

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Experimental realization of phonon demultiplexing in three-dimensions

arXiv

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arXiv 2020年

作者： Bilal, Osama R. Yee, Chern Hwee Rys, Jan Schumacher, Christian Daraio, Chiara Department of Mechanical engineering University of Connecticut Storrs United States Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland Computer Graphics Laboratory ETH Zürich Zürich8092 Switzerland Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States

Phononic metamaterials enabled the realization of many acoustic components analogous to their electronic counterparts, such as transistors, logic gates and calculators. A key component among these is the demultiplexer, a device that receives multiple signals and sorts them based on their frequencies into separate channels. Previous experimental realizations of acoustic and elastic multiplexers have employed plates with pillars or holes to demultiplex frequencies. However, existing realizations are confined to two-dimensions, which can limit potential acoustic or elastic circuit design. Here we show the first experimental realization of a three-dimensional, four channel phononic demultiplexer. Our design methodology is based on bundles of pass-bands within a large band gap that can easily be tuned for multi-channel frequency demultiplexing. The proposed design can be utilized in acoustic and elastic information processing, nondestructive evaluation and communication applications among others. Copyright © 2020, The Authors. All rights reserved.

关键词： Demultiplexing

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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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Flat Electroencephalography Image: Image Size Dependent Normalization versus Fuzzy Technique 12

Flat Electroencephalography Image: Image Size Dependent Norm...

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12th Seminar on science and Technology 2018

作者： Zenian, Suzelawati Ahmad, Tahir Idris, Amidora Department of Mathematics with Computer Graphics Faculty of Science and Natural Resources Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Centre for Sustainable Nanomaterials Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

Flat Electroencephalography (fEEG) is a method for mapping high dimensional signal, namely Electroencephalography (EEG) into a low dimensional space. The image of fEEG which is in grayscale form is obtained from digital fEEG by using fuzzy approach. The main aim of this paper is to reduce the spread of the vague boundary and improve the visibility of the clusters of epileptic foci in terms of contrast enhancement via Image Size Dependent Normalization (ISDN) and fuzzy technique. Contrast performance comparison between both methods are carried out for an epileptic patient at varied time, t. It shows that fuzzy method gives better contrast compared to ISDN. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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Medical deep learning-A systematic meta-review

arXiv

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arXiv 2020年

作者： Egger, Jan Gsaxner, Christina Pepe, Antonio Pomykala, Kelsey L. Jonske, Frederic Kurz, Manuel Li, Jianning Kleesiek, Jens Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Inffeldgasse 16 Styria Graz8010 Austria Department of Oral & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz 5/1 Styria Graz8036 Austria Computer Algorithms for Medicine Laboratory Styria Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany

Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research effort s. In Q2/2020, the search engine PubMed returned already over 11,000 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys. Copyright © 2020, The Authors. All rights res

关键词： Generative adversarial networks

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