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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Torsten Bolke Santhosh Seshadhri Ozlem Gurvit Ragnar Bade Bernhard Preim Gabor Janiga Martin Skalej Steffen Serowy Georg Rose Healthcare Telematics & Medical Engineering Group Electrical Engineering Department Otto-von-Guericke University of Magdeburg Germany Laboratory of Fluid Dynamics and Technical Flows Process and Systems Engineering Department Otto-von-Guericke University of Magdeburg Germany Institute for Neuroradiology Medical Department Otto-von-Guericke University of Magdeburg Germany Visualization Research Group Computer Science Department Otto-von-Guericke University of Magdeburg Germany

In this paper we investigate the dynamics of contrast agent concentration as blood flow estimation method utilizing contributions from three different fields: morphologic and dynamic angiographic X-ray scans, computational fluid dynamics (CFD) simulations and Laser-Doppler-Velocimetry (LDV) measurements. Based on dynamic angiographic X-ray measurements we exploit dedicated CFD simulations, carried out specifically for each target object, for the adaptation of the plain video densitometric results. Finally, a comparison with LDV is presented.

关键词： Imaging phantoms Angiography Computational fluid dynamics Blood flow X-ray lasers Fluid dynamics Computational modeling Analytical models Fluid flow measurement Image analysis

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Parallel cell projection rendering of adaptive mesh refinement data

Parallel cell projection rendering of adaptive mesh refineme...

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IEEE Symposium on Parallel and Large-Data visualization and graphics (PVG)

作者： G.H. Weber M. Ohler O. Kreylos J.M. Shalf E.W. Bethel B. Hamann G. Scheuermann Visualization Group National Energy Research Scientific Computing Center (NERSC) Lawrence Berkeley National Laboratory Berkeley CA USA AG Graphische Datenverabeitung und Computergeometrie FB Informatik University of Kaiserslautern Kaiserslautern Germany Center for Image Processing and Integrated Computing Center for Image Processing and Integrated Computing (CIPIC) Department of Computer Science University of California Davis CA USA Center for Image Processing and Integrated Computing Department of Computer Science University of California Davis CA USA

Adaptive mesh refinement (AMR) is a technique used in numerical simulations to automatically refine (or de-refine) certain regions of the physical domain in a finite difference calculation. AMR data consists of nested hierarchies of data grids. As AMR visualization is still a relatively unexplored topic, our work is motivated by the need to perform efficient visualization of large AMR data sets. We present a software algorithm for parallel direct volume rendering of AMR data using a cell-projection technique on several different parallel platforms. Our algorithm can use one of several different distribution methods, and we present performance results for each of these alternative approaches. By partitioning an AMR data set into blocks of constant resolution and estimating rendering costs of individual blocks using an application specific benchmark, it is possible to achieve even load balancing.

关键词： Adaptive mesh refinement Data visualization Rendering (computer graphics) Spatial resolution Partitioning algorithms Load management Parallel programming graphics Physics computing Computational efficiency

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Performance of the H.263 Video Compression Standard

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Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology 1997年第2-3期17卷 101-111页

作者： Girod, Bernd Steinbach, Eckehard Färber, Nikolaus Telecommunications Institute University of Erlangen-Nuremberg 91058 Erlangen Cauerstrasse 7 Germany Georgia Institute of Technology United States University of Hannover Germany Inst. F. Theor. N. University of Hannover Germany Massachussetts Inst. of Technology Cambridge MA United States Research Laboratory of Electronics United States Department of Media Technology Media Laboratory Germany Department of Computer Graphics Germany Academy of Media Arts Cologne Germany Computer Science Section Cologne University Germany Digital Signal Processing Group Georgia Institute of Technology Atlanta GA United States Dept. of Elec. Eng./Telecom. University of Erlangen-Nuremberg Germany Telecommunications Institute Germany Vivo Software Inc. Waltham MA United States University of Karlsruhe Germany University of Essex United Kingdom ESIEE Paris France Image Communication Group Telecommunications Institute University of Erlangen-Nuremberg Germany University of Munich Germany Telecommunications Institute University of Erlangen-Nuremberg Germany Image Communication Group

The draft international standard ITU-T H.263 is closely related to the well known and widely used ITU-T Recommendation H.261. However, H.263 does provide the same subjective image quality at less than half the bit-rate. In this paper we investigate to what extend single enhancements of H.263 contribute to this performance gain, and consider the trade-off quality vs. complexity. Based on typical test sequences, H.263 with its various modes is compared to H.261 on the basis of rate distortion curves at bit-rates up to 128 kbps. At 64 kbps, the performance gain of H.263 in its default mode compared to H.261 is approximately 2 dB. This improvement is achieved with only little increase of complexity, and is mainly due to more accurate motion compensation with half-pel accuracy. Considering the trade-off quality vs. complexity, the combination of the optional coding-modes "Advanced prediction mode" and "PB-frames mode" is a good compromise, resulting in an additional performance gain of 1.5 dB PSNR at 64 kbps. The "Syntax-based arithmetic coding mode" on the other hand, offers only a very small performance gain (0.1 dB at 64 kbps) for its increased computational complexity. Results from profiling an H.263 software codec are presented in order to support complexity considerations of the optional coding-modes.

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EFaR 2023: Efficient Face Recognition Competition

arXiv

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian 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 Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology 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. © 2023, CC BY-NC-SA.

关键词： Face recognition

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

arXiv

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks. © 2023, CC BY-NC-SA.

关键词： Competition

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

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Development of a computer-aided design software for dental splint in orthognathic surgery

arXiv

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arXiv 2017年

作者： Chen, Xiaojun Li, Xing Xu, Lu Sun, Yi Politis, Constantinus Egger, Jan Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China OMFS IMPATH Research Group Department of Imaging and Pathology Faculty of Medicine KU Leuven Leuven Belgium Department of Oral and Maxillofacial Surgery University Hospitals Leuven Leuven Belgium Faculty of Computer Science and Biomedical Engineering Institute for Computer Graphics and Vision Graz University of Technology Graz Austria BioTechMed-Graz Graz Austria

In the orthognathic surgery, dental splints are important and necessary to help the surgeon reposition the maxilla or mandible. However, the traditional methods of manual design of dental splints are difficult and time-consuming. The research on computer-aided design software for dental splints is rarely reported. Our purpose is to develop a novel special software named EasySplint to design the dental splints conveniently and efficiently. The design can be divided into two steps, which are the generation of initial splint base and the Boolean operation between it and the maxilla-mandibular model. The initial splint base is formed by ruled surfaces reconstructed using the manually picked points. Then, a method to accomplish Boolean operation based on the distance filed of two meshes is proposed. The interference elimination can be conducted on the basis of marching cubes algorithm and Boolean operation. The accuracy of the dental splint can be guaranteed since the original mesh is utilized to form the result surface. Using EasySplint, the dental splints can be designed in about 10 minutes and saved as a stereo lithography (STL) file for 3D printing in clinical applications. Three phantom experiments were conducted and the efficiency of our method was demonstrated. Copyright © 2017, The Authors. All rights reserved.

关键词： computer software

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The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias*

The Unconstrained Ear Recognition Challenge 2023: Maximizing...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ž. Emeršić T. Ohki M. Akasaka T. Arakawa S. Maeda M. Okano Y. Sato A. George S. Marcel I. I. Ganapathi S. S. Ali S. Javed N. Werghi S. G. Işık E. Sarıtaş H. K. Ekenel V. Hudovernik J. N. Kolf F. Boutros N. Damer G. Sharma A. Kamboj A. Nigam D. K. Jain G. Cámara-Chávez P. Peer V. Štruc University of Ljubljana (UL SI) Shizuoka University (SU JP) Idiap Research Institute (IDIAP CH) Electrical Engineering and Computer Science Department Khalifa University (KU UAE) Department of Computer Engineering (ITU TR) Istanbul Technical University Microelectronic Guidance and Electro-Optical Group ASELSAN Fraunhofer Institute for Computer Graphics Research IGD (IGD DE) Technische Universität Darmstadt (TUD DE) Indian Institute of Technology Mandi (IIT IN) HCL Imaging and Robotics R&D Lab (HCL IN) School of Artificial Intelligence Dalian University of Technology (DLUT CN) Federal University of Ouro Preto (UFOP BR)

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://***/

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EFaR 2023: Efficient Face Recognition Competition

EFaR 2023: Efficient Face Recognition Competition

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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

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Impact of Implementing Game Elements in Gamifying Educational Environment: A Study

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Journal of Physics: Conference Series 2019年第1期1358卷

作者： F Durin R Lee A Bade C K On N Hamzah Mathematics Graphics and Visualization Research Group (M-GRAVS) Faculty of Science and Natural Resources Universiti Malaysia Sabah 88450 Kota Kinabalu Sabah Malaysia Mathematics and Computer Graphics Faculty of Science and Natural Resources Universiti Malaysia Sabah Center of Excellence in Semantic Agents Universiti Malaysia Sabah Sabah Malaysia

Gamification is the use of game elements in a non-gaming environments. The aims of implementing game elements in gamifying educational environment is to make the learners engaged and motivated in the learning process. But, some of the research result giving bad impacts in implementing gamification in education. The objective of this research is to study the impact of implementing game elements in education. The aim of this research is to study which game elements is most commonly used in gamifying educational environment. The study is done by reviewing previous research on impacts of implementing game element in education from 2008 until 2018 to see whether the elements giving positive or negative impacts on education. The results shows the most common game elements used in gamification is rewards, feedback, challenge, quest/mission/goal, level/stage, point/score, avatars/players, task, character, time-limit, narrative/dialogue, leaderboards, progress bars, and badges. Majority of the research on gamification in education reports positive impacts by introducing gamification in education. In conclusion, by implementing game elements in the gamifying educational environment is a good method to create a good adjustment in learners' behavior and assertiveness in the learning process, it can develop engagement and motivation of the learners'.

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