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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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Smart Cloud System for Forensic Thermal Image Enhancement Using Local and Global Logarithmic Transform Histogram Matching

Smart Cloud System for Forensic Thermal Image Enhancement Us...

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Viacheslav Voronin Evgenii Semenishchev Vladimir Frants Sos Agaian Lab. “Mathematical methods of image processing and intelligent computer vision systems” Don State Technical University Rostov-on-Don Russian Federation “STANKIN” Moscow State University of Technology Moscow Russian Federation Dept. of Computer Science CUNY/The College of Staten Island Staten Island New York United States

Digital images used in the investigation of a crime often undergo several concurrent enhancement operations for improved automated analysis. The challenges are related to the big size of data and complexity of the forensic image processing. Our purpose is providing a smart cloud system to image processing for PC and Smartphones with limited computation complexity. This paper presents a new thermal image enhancement algorithm based on combined local and global image processing in the frequency domain. The presented approach uses the fact that the relationship between stimulus and perception is logarithmic. The basic idea is to apply logarithmic transform histogram matching with spatial equalization approach on different image blocks. The resulting image is a weighted mean of all processing blocks. The weights for every local and global enhanced image driven through optimization of measure of enhancement (EME). Some presented experimental results illustrate the performance of the proposed cloud system on real thermal images in comparison with the traditional methods.

关键词： Histograms Transforms Image enhancement Forensics Adaptive equalizers Clouds

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Blind Visual Quality Assessment for Smart Cloud-Based Video Storage

Blind Visual Quality Assessment for Smart Cloud-Based Video ...

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Vladimir Frants Viacheslav Voronin Alexander Zelenskiy Sos Agaian Metrological lab. “Small GIC” Moscow State University of Technology “STANKIN” Moscow Russian Federation Pro-rector for Research Work and R&D Politics Moscow State University of Technology “STANKIN” Moscow Russian Federation Lab. “Mathematical methods of image processing and intelligent computer vision systems.” Don State Technical University Rostov-on-Don Russian Federation Dept. of Computer Science CUNY The College of Staten Island Staten Island New York United States

In this paper, we present a new video quality metric targeted for use within cloud-based video storage systems. Because of the limited capacity of storage solutions currently in use, it is common for stored videos to have low perceived video quality. Ability to predict the quality of coded video sequence in fully automatic fashion is crucial for optimal coding parameters selection. We have developed general quality estimation approach applicable for different kinds of video content and useful for online correction of perceived quality of stored video sequence. Proposed objective video quality metric is designed to have a high level of correlation with human-based quality assessment results. A high level of conformity of predicted video quality to perceived quality level is achieved by using a convolutional neural network trained on a large volume of video data in the framework of generative adversarial learning and combination of carefully selected regularization techniques. Evaluation of developed VQA metric on commonly used datasets shows equal or better correlation with MOS than the current state of the art approaches.

关键词： Quality assessment Measurement Video recording Video sequences Streaming media Training Visualization

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Vehicle density, vehicle speed and packet inter-arrival time analysis in IEEE 802.11p EDCA based VANETs

Vehicle density, vehicle speed and packet inter-arrival time...

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International Conference on signal, Image, Vision and their Applications (SIVA )

作者： Yacine Harkat Abderrahmane Amrouche Division Microélectronique et Nanotechnologie (DMN) Centre de Développement des Technologies Avancées (CDTA) Speech Com. Signal Proc. Lab. FEI USTHB Cité 20 août 1956 Baba Hassen Algiers Algeria Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Sciences USTHB P.O. Box 32 El Alia Bab Ezzouar Algiers Algeria

ISBN: (纸本)9781538671214;9781538671207

Vehicular ad-hoc networks (VANETs) are considered as a key component of future Intelligent Transportation Systems (ITS). The IEEE 802.11p standard modifies the IEEE 802.11a to adapt to vehicular networks. In the MAC sublayer, the IEEE 802.11p standard uses Enhanced Distributed Channel Access (EDCA) mechanism to provide differentiated media access by defining four access categories (ACs). However, VANET characteristics such as network size, high speed, fast topology changes, traffic density, can greatly affect network performance, especially for the MAC sub-layer. Therefore, important issues arise about the ability of this standard to provide reliable and efficient services for vehicular environment. Since test beds are still limited, computer simulations are very useful for the automobile industry. This paper present a simulation-based analysis of the impact of the vehicle density, vehicle speed and packet inter-arrival time on the performance of the IEEE 802.11p EDCA mechanism in VANET. Throughput, average delay and packet delivery ratio (PDR) were considered as evaluation metrics. The obtained results confirm that IEEE 802.11p EDCA provides an effective differentiation service mechanism that can be adapted to critical ITS applications. However, we noted a performance degradation in extreme cases. As in large number of communicating vehicles, very high speeds, and high transmission rates.

关键词： Throughput Delays Standards Vehicular ad hoc networks Data models Mobile computing

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Corrigendum to “Integrated production and transportation on parallel batch machines to minimize total weighted delivery time” [computers and Operations Research Volume 102, pages 39–51]

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computers & Operations Research 2019年 105卷 248-248页

作者： Zhao-hong Jia Xue-xue Zhuo Joseph Y.-T. Leung Kai Li Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui 230039 P.R. China Department of Computer Science New Jersey Institute of Technology Newark NJ 07102 USA School of Management Hefei University of Technology Hefei Anhui 230009 P.R. China

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Multi-View Images Fusion Model

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IOP Conference Series: Materials Science and Engineering 2019年第1期680卷

作者： M M Zhdanova V V Voronin R A Sizyakin M S Minkin A A Zelensky Lab. 'Mathematical methods of image processing and intelligent computer vision systems' Don State Technical University Gagarin sq. 1 Rostov-on-Don 344000 Russia Moscow State University of Technology 'STANKIN' Vadkovsky line 1 Moscow 127055 Russia

The tasks of recognition actions and classification objects are fundamental in computer vision systems. Even subtasks, such as recognition of atomic motion and single objects form the basis for understanding the situation in the work area and the scene in general. This is especially important in video surveillance systems designed to ensure security. Thus, the effectiveness of recognition and classification methods is one of the primary tasks of computer vision. But the visual methods implemented in similar video surveillance systems, encounter some difficulties, such as inhomogeneous background, uncontrolled operating environments, irregular illumination, etc. To address these drawbacks, the paper presents a model for combining visible range images and depth images. This model allows to improve the quality of recognized images, provides the construction of a more informative descriptor, which also positively affects the recognition efficiency. Our results show that it has good performance in fusion visible image and depth map.

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WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

arXiv

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

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Iterative harmonic speech enhancement

Iterative harmonic speech enhancement

引用

12. ITG-Fachtagung Sprachkommunikation - 12th ITG Conference on Speech Communication

作者： Stahl, Johannes Mowlaee, Pejman Signal Processing and Speech Communication Lab. Graz University of Technology Graz8010 Austria

ISBN: (纸本)9783800742752

Several iterative approaches have been proposed for speech enhancement. This work reviews these methods and further presents a novel iterative estimation scheme to jointly estimate the harmonic parameters, i.e., amplitude, frequency and phase of a speech signal observed in noise. The potential of the proposed iterative method in terms of harmonic parameter estimation is analyzed on synthetic harmonic signals. Further, by applying our proposed method on noise-corrupted speech files at different signal-to-noise ratios, we demonstrate its effectiveness in improving in-strumentally predicted speech intelligibility and perceived speech quality. © 2016 VDE VERLAG GMBH.

关键词： Speech enhancement

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