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arXiv 2019年

作者： Damer, Naser Boutros, Fadi Mallat, Khawla Kirchbuchner, Florian Dugelay, Jean-Luc Kuijper, Arjan Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Digital Security Department EURECOM Sophia Antipolis France

Generating visible-like face images from thermal images is essential to perform manual and automatic cross-spectrum face recognition. We successfully propose a solution based on cascaded refinement network that, unlike previous works, produces high quality generated color images without the need for face alignment, large databases, data augmentation, polarimetric sensors, computationally-intense training, or unrealistic restriction on the generated resolution. The training of our solution is based on the contextual loss, making it inherently scale (face area) and rotation invariant. We present generated image samples of unknown individuals under different poses and occlusion conditions. We also prove the high similarity in image quality between ground-truth images and generated ones by comparing seven quality metrics. We compare our results with two state-of-the-art approaches proving the superiority of our proposed approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

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Progressive bilateral-context driven model for post-processing person Re-identification

arXiv

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arXiv 2020年

作者： Cao, Min Chen, Chen Dou, Hao Hu, Xiyuan Peng, Silong Kuijper, Arjan School of Computer Science and Technology Soochow University Suzhou215006 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Fraunhofer IGD Darmstadt64283 Germany Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Computer Science and Engineering Nanjing University of Science and Technology China Mathematical and Applied Visual Computing TU Darmstadt Darmstadt64283 Germany

Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities, these content-based methods that determine the pairwise relationship only based on the similarity between them, inevitably produce a suboptimal ranking list. Instead, the pairwise similarity can be estimated more accurately along the geodesic path of the underlying data manifold by exploring the rich contextual information of the sample. In this paper, we propose a lightweight post-processing person re-identification method in which the pairwise measure is determined by the relationship between the sample and the counterpart’s context in an unsupervised way. We translate the point-to-point comparison into the bilateral point-to-set comparison. The sample’s context is composed of its neighbor samples with two different definition ways: the first order context and the second order context, which are used to compute the pairwise similarity in sequence, resulting in a progressive post-processing model. The experiments on four large-scale person re-identification benchmark datasets indicate that (1) the proposed method can consistently achieve higher accuracies by serving as a post-processing procedure after the content-based person re-identification methods, showing its state-of-the-art results, (2) the proposed lightweight method only needs about 6 milliseconds for optimizing the ranking results of one sample, showing its high-efficiency. Code is available at: https://***/123ci/PBCmodel. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Background subtraction with real-time semantic segmentation

arXiv

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arXiv 2018年

作者： Zeng, Dongdong Chen, Xiang Zhu, Ming Goesele, Michael Kuijper, Arjan Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China University of Chinese Academy of Sciences Beijing100049 China Fraunhofer Igd Darmstadt64283 Germany Graphics Capture and Massively Parallel Computing Tu Darmstadt Darmstadt64283 Germany Mathematical and Applied Visual Computing Tu Darmstadt Darmstadt64283 Germany

Accurate and fast foreground object extraction is very important for oBackgroundracking and recognition mancideo surveillance. AlthougS btraction background subtracSegmeation GS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). Our proposed fram Fework consists of two components, a traditional BGS segmenter B and a real-time semantic segmenter S. The BGS segmenter B aims to construct background models and segments foreground objects. The realtime semantic segmenter S is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. B and S work in parallel on two threads. For each input frame It, the BGS segmenter B computes a preliminary foreground/background (FG/BG) mask Bt. At the same time, the real-time semantic segmenter S extracts the object-level semantics St. Then, some specifi c rules are applied on Bt and Stto generate the final detae)ction Dt. Finally, the refined FG/BG mask Dtis fed back to update the background model. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves state-of-the-art performance among all unsupervised background subtraction methods while operating at real-time, and even performs better than some deep learning based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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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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Trust the biometrie mainstream: Multi-biometric fusion and score coherence

Trust the biometrie mainstream: Multi-biometric fusion and s...

引用

European Signal Processing Conference (EUSIPCO)

作者： Naser Damer Chadi Izzou Rhaibani Andreas Braun Arjan Kuijper Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing group Technische Universität Darmstadt Darmstadt Germany

Multi-biometrics aims at building more accurate unified biometric decisions based on the information provided by multiple biometric sources. Information fusion is used to optimize the process of creating this unified decision. In previous works dealing with score-level multi-biometric fusion, the scores of different biometric sources belonging to the comparison of interest are used to create the fused score. This is usually achieved by assigning static weights for the different biometric sources with more advanced solutions considering supplementary dynamic information like sample quality and neighbours distance ratio. This work proposes embedding score coherence information in the fusion process. This is based on our assumption that a minority of biometric sources, which points out towards a different decision than the majority, might have faulty conclusions and should be given relatively smaller role in the final decision. The evaluation was performed on the BioSecure multimodal biometric database with different levels of simulated noise. The proposed solution incorporates, and was compared to, three baseline static weighting approaches. The enhanced performance induced by including the coherence information within a dynamic weighting scheme in comparison to the baseline solution was shown by the reduction of the equal error rate by 45% to 85% over the different test scenarios and proved to maintain high performance when dealing with noisy data.

关键词： Coherence Databases Noise measurement Face Europe Probes Error analysis

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A Repository Infrastructure for Working with 3D Assets in Cultural Heritage

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International Journal of Heritage in the Digital Era 2013年第1期2卷 143-166页

作者： Xueming Pan Martin Schröttner Sven Havemann Thomas Schiffer René Berndt Martin Hecher Dieter W. Fellner Institute of Computer Graphic and Knowledge Visualization Graz University of Technology 8010 Graz Austria Fraunhofer Austria Research GmbH Visual Computing 8010 Graz Austria GRIS TU Darmstadt & Fraunhofer IGD 64283 Darmstadt Germany

The development of a European market for digital cultural heritage assets is impeded by the lack of a suitable digital marketplace, i.e., a commonly accepted exchange platform for digital assets. We have developed the technology for such a platform over the last two years: The 3D-COFORM Repository Infrastructure (RI) is a secure content management infrastructure for the distributed processing of large-volume datasets. Three of the key features of this system are (1) owners have complete control over their data, (2) binary data must have attached metadata, and (3) processing histories are documented. Our system can support the complete production pipeline for digital assets from data acquisition (photo, 3D scan) over processing (cleaning, hole filling) to interactive presentation and content delivery over the internet. In this paper we present the components of the system and their interplay. One particular focus of the software development was to make it as easy as possible to connect client-side applications to the RI. Therefore we present the RI-API in some detail and present several RI-enabled client-side applications that use it.

关键词： 3D data processing content management system 3D repository paradata CIDOC-CRM CRMdig digital marketplace web services

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An enhanced distributed repository for working with 3D assets in cultural heritage

引用

4th International Conference on Cultural Heritage, EuroMed 2012

作者： Pan, Xueming Schiffer, Thomas Schröttner, Martin Berndt, René Hecher, Martin Havemann, Sven Fellner, Dieter W. Institute of Computer Graphic and Knowledge Visualization Graz University of Technology 8010 Graz Austria Visual Computing Fraunhofer Austria Research GmbH 8010 Graz Austria GRIS TU Darmstadt and Fraunhofer IGD 64283 Darmstadt Germany

ISBN: (纸本)9783642342332

The development of a European market for digital cultural heritage assets is impeded by the lack of a suitable marketplace, i.e., a commonly accepted distributed exchange platform for digital assets. We have developed such a platform over the last two years, a centralized content management system with distributed storage capability and semantic query functionality. It supports the complete pipeline from data acquisition (photo, 3D scan) over processing (cleaning, hole filling) to interactive presentation, and allows collecting a complete process description (paradata) alongside. In this paper we present the components of the system and explain their interplay. Furthermore, we present and explain which functional components, from transactions to permission management, are needed to operate the system. Finally, we prove the suitability of the API and present a few software applications that use it. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Three dimensional computer graphics

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Efficient Volumetric Anisotropic Diffusion Computation on GPUs

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PAMM 2012年第1期12卷

作者： Arjan Kuijper Andreas Schwarzkopf Thomas Kalbe Michael Goesele Fraunhofer IGD - Institute for applied research in Visual Computing Darmstadt Germany Interactive Graphics Systems Group Technische Universität Darmstadt Germany

We present an efficient implementation of volumetric anisotropic image diffusion on modern programmable graphics processing units (GPUs). We avoid the computational bottleneck of a time consuming eigenvalue decomposition in ℝ 3 . Instead, we use a projection of the Hessian matrix along the surface normal onto the tangent plane of the local isodensity surface and solve for the remaining two tangent space eigenvectors. We derive closed formulas to achieve this resulting in efficient GPU code. Our most complex volumetric anisotropic diffusion gains a speed up of more than 600 compared to a CPU solution [1]. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)

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Ambient Intelligence 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： David V. Keyson Mary Lou Maher Norbert Streitz Adrian Cheok Juan Carlos Augusto Reiner Wichert Gwenn Englebienne Hamid Aghajan Ben J. A. Kröse

ISBN: (数字)9783642251672

ISBN: (纸本)9783642251665

This book constitutes the refereed proceedings of the Second International Joint Conference on Ambient Intelligence, AmI 2011, held in Amsterdam, The Netherlands, in November 2011. The 58 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics such as haptic interfaces, smart sensing, smart environments, novel interaction technologies, affecting human behaviour, privacy and trust, landscape and ambient assisted living.

关键词： Artificial Intelligence Information Systems Applications (incl. Internet) Computer Communication Networks User Interfaces and Human Computer Interaction Software Engineering Computers and Society

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