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IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

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IEEE BigData 2023 Keystroke Verification Challenge (KVC)

arXiv

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arXiv 2024年

作者： Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami DeAndres-Tame, Ivan Damer, Naser Fierrez, Julian Garcia, Javier-Ortega Gonzalez, Nahuel Shadrikov, Andrei Gordin, Dmitrii Schmitt, Leon Wimmer, Daniel Großmann, Christoph Krieger, Joerdis Heinz, Florian Krestel, Ron Mayer, Christoffer Haberl, Simon Gschrey, Helena Yamagishi, Yosuke Saha, Sanjay Rasnayaka, Sanka Wickramanayake, Sandareka Sim, Terence Gutfeter, Weronika Baran, Adam Krzyszton, Mateusz Jaskóla, Przemyslaw Lab Universidad Autonoma de Madrid Spain Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Universidad de Buenos Aires Argentina Verigram LLC Singapore Citix Almaty Kazakhstan Regensburg University of Applied Sciences Germany Division of Radiology and Biomedical Engineering The University of Tokyo Japan National University of Singapore Singapore University of Moratuwa Sri Lanka NASK - National Research Institute Warsaw Poland

This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge1 (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on CodaLab2, the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field. © 2024, CC BY-NC-ND.

关键词： Biometrics

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Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks

arXiv

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arXiv 2021年

作者： Grandits, Thomas Pezzuto, Simone Costabal, Francisco Sahli Perdikaris, Paris Pock, Thomas Plank, Gernot Krause, Rolf Institute of Computer Graphics and Vision TU Graz Graz Austria BioTechMed-Graz Graz Austria Center for Computational Medicine in Cardiology Institute of Computational Science Università della Svizzera italiana Lugano Switzerland Department of Mechanical and Metallurgical Engineering School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Institute for Biological and Medical Engineering Schools of Engineering Medicine and Biological Sciences Pontificia Universidad Católica de Chile Santiago Chile Millennium Nucleus for Cardiovascular Magnetic Resonance Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA United States Gottfried Schatz Research Center Division of Biophysics Medical University of Graz Graz Austria

Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases, with an RMSE of 2.2ms on the in-silico data and outperforming a state of the art method on the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Tensors

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Wide-range Angle-sensitive Plasmonic Color Printing on Lossy-Resonator Substrates

arXiv

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arXiv 2023年

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Pagadala, Karthik Nyga, Piotr Fruhling, Colton Bravo, Esteban Garcia Maćkowski, Sebastian Shalaev, Vladimir M. Kildishev, Alexander V. Boltasseva, Alexandra Elmore Family School of Electrical & Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

We demonstrate a sustainable, lithography-free process for generating non fading plasmonic colors with a prototype device that produces a wide range of vivid colors in red, green, and blue (RGB) ([0-1], [0-1], [0-1]) color space from violet (0.7, 0.72, 1) to blue (0.31, 0.80, 1) and from green (0.84, 1, 0.58) to orange (1, 0.58, 0.46). The proposed color-printing device architecture integrates a semi-transparent random metal film (RMF) with a metal back mirror to create a lossy asymmetric Fabry-Pérot resonator. This device geometry allows for advanced control of the observed color through the five-degree multiplexing (RGB color space, angle, and polarization sensitivity). An extended color palette is then obtained through photomodification process and localized heating of the RMF layer under various femtosecond laser illumination conditions at the wavelengths of 400 nm and 800 nm. Colorful design samples with total areas up to 10 mm2 and 100 μm resolution are printed on 300-nm-thick films to demonstrate macroscopic high-resolution color generation. The proposed printing approach can be extended to other applications including laser marking, anti-counterfeiting and chromo-encryption. © 2023, CC BY.

关键词： Color

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KVC-onGoing: Keystroke Verification Challenge

arXiv

引用

arXiv 2024年

作者： Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami DeAndres-Tame, Ivan Damer, Naser Fierrez, Julian Ortega-Garcia, Javier Acien, Alejandro Gonzalez, Nahuel Shadrikov, Andrei Gordin, Dmitrii Schmitt, Leon Wimmer, Daniel Großmann, Christoph Krieger, Joerdis Heinz, Florian Krestel, Ron Mayer, Christoffer Haberl, Simon Gschrey, Helena Yamagishi, Yosuke Saha, Sanjay Rasnayaka, Sanka Wickramanayake, Sandareka Sim, Terence Gutfeter, Weronika Baran, Adam Krzyszton, Mateusz Jaskóla, Przemyslaw Lab Universidad Autonoma de Madrid Spain Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Universidad de Buenos Aires Argentina Verigram LLC Singapore Citix Almaty Kazakhstan Regensburg University of Applied Sciences Germany Division of Radiology and Biomedical Engineering The University of Tokyo Japan National University of Singapore Singapore University of Moratuwa Sri Lanka NASK – National Research Institute Warsaw Poland

This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing)1, on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards simulating real-life conditions. The results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics, reaching values as low as 3.33% of Equal Error Rate (EER) and 11.96% of False Non-Match Rate (FNMR) @1% False Match Rate (FMR) in the desktop scenario, and 3.61% of EER and 17.44% of FNMR @1% at FMR in the mobile scenario, significantly improving previous state-of-the-art results. Concerning demographic fairness, the analyzed scores reflect the subjects’ age and gender to various extents, not negligible in a few cases. The framework runs on CodaLab2 © 2024, CC BY-NC-ND.

关键词： Biometrics

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Erratum to June-issue Table of Contents

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IEEE Transactions on Biomedical engineering 2006年第8期53卷 1728-1728页

作者： C. Vidaurre A. Schlogl R. Cabeza R. Scherer G. Pfurtscheller Department of Electrical Engineering and Electonics State University of Navarra Pamplona Spain Institute of Human-Computer Interfaces BCI-Lab Institute for Computer Graphics Graz University of Technology Graz Austria

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CAD and Robotics in Architecture and Construction 1

引用

1000年

作者： A. Bijl R. Krishnamurti O. Akin C.-C. Chen B. Dave S. Pithavadian G. Schmitt J.-C. Robert J. Weeks U. Flemming R. Coyne T. Glavin Y. E. Kalay A. C. Harfmann L. M. Swerdloff M. Rychener L. Koskela R. Hynynen M. Kallavuo K. Kahkönen J. Salokivi A. H. Bridges A. Polistina W. L. Whittaker E. Bandari B. Motazed Y. Hasegawa C. Abel A. H. Slocum R. Kangari M.-C. Wanner M. Skibniewski P. Derrington C. Hendrickson R. F. Woodbury W. T. Keirouz I. J. Oppenheim D. R. Rehak C. F. Earl N. Kano J. L. Crowley P. J. Drazan H.-R. Oeser N. Tanaka M. Saito K. Arai K. Banno T. Ochi S. Kikuchi T. Ueno J. Maeda T. Yoshida S. Suzuki

ISBN: (数字)9781468474046

ISBN: (纸本)9781850912538

After two decades, data processing has finally, and probably forever, found its niche among civil engineering and construction (CEC) professionnals, through word processors, digitizing tables, management software, and increasingly via drawing software and computer-aided design (CAD), recently, robots have even started invading work sites. What are the main trends of CAD and robotics in the field of architecture and civil enginee ring? What type of R&D effort do university and industrial laboratories undertake to devise the professional software that will be on the market in the next three to five years? These are the issues which will be addressed during this symposium. To this effect, we have planned concurrently an equipment and software show, as well as a twofold conference. Robotic is just starting in the field of civil engineering and construction. A pioneer, the Civil engineering Departement of Carnegie-Mellon University, in the United States, organized the first two international symposia, in 1984 and 1985 in Pittsburgh. This is the third meeting on the subject (this year, however, we have also included CAD). It constitutes the first large international symposium where CAD experts, specialists in architecture and CEC robotics will meet. From this standpoint, it should be an ideal forum for exchanging views and expe riences on a wide range of topics, and we hope it will give rise to novel applications and new syntheses. This symposium is intented for scientists, teachers, students and also for manufacturers and all CEC professionals.

关键词： Robotics and Automation Science, Humanities and Social Sciences, multidisciplinary

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In-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in ultrasound acquisitions of the liver with two specialists in Internal Medicine

In-depth assessment of an interactive graph-based approach f...

引用

Biomedical engineering International Conference (BMEiCON)

作者： Jan Egger Dieter Schmalstieg Lucas Bettac Mark Hänle Alexander Hann Xiaojun Chen Tilmann Gräter Wolfram G. Zoller BioTechMed-Graz Graz Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Department of Internal Medicine I Ulm University Ulm Germany Department of Internal Medicine and Gastroenterology Katharinenhospital Stuttgart Germany School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Department of Diagnostic and Interventional Radiology Ulm University Ulm Germany

The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in US images of the liver with two specialists in Internal Medicine. Thereby, evaluating the approach with over one hundred different acquisitions of metastases. The two physicians or the algorithm had never assessed the acquisitions before the evaluation. In summary, the physicians first performed a pure manual outlining followed by an algorithmic segmentation over one month later. As a result, the experts satisfied in up to ninety percent of algorithmic segmentation results. Furthermore, the algorithmic segmentation was much faster than manual outlining and achieved a median Dice Similarity Coefficient (DSC) of over eighty percent. Ultimately, the algorithm enables a fast and accurate segmentation of liver metastasis in clinical US images, which can support the manual outlining in daily practice.

关键词： Ultrasonic imaging Image segmentation Liver Manuals Metastasis Lesions

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

arXiv

引用

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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Medical deep learning-A systematic meta-review

arXiv

引用

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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