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Segmentation of pedestrians in thermal imaging

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Segmentation of pedestrians in thermal imaging

2018 Baltic URSI Symposium, URSI 2018

作者： Piniarski, Karol Pawlowski, Pawel Division of Signal Processing and Electronic Systems Faculty of Computing Poznan University of Technology Poznań Poland

ISBN: (纸本)9788394942137

Image segmentation is one of crucial stages of video processing used for detection of pedestrians in infrared (IR) vision. The paper presents a study of passive (typically far IR) technologies for IR vision and an analysis of the state-of-the-art in the extraction of information from IR images. A deep testing of segmentation methods was performed for IR images with a specially prepared benchmark of 162 IR video frames with pedestrians. The initial tests included two segmentation methods: The single threshold and the Otsu method. The obtained results are promising as the effectiveness reaches ca. 82% for both techniques. © 2018 Warsaw University of Technology.

关键词： Image segmentation

Novel deep hybrid model for electricity price prediction based on dual decomposition

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Applied Energy 2025年 395卷

作者： Sujan Ghimire Thong Nguyen-Huy Ravinesh C. Deo David Casillas-Pérez A.A. Masrur Ahmed Sancho Salcedo-Sanz Artificial Intelligence Applications Laboratory School of Mathematics Physics and Computing University of Southern Queensland Springfield QLD 4300 Australia Centre for Applied Climate Sciences University of Southern Queensland Toowoomba 4350 QLD Australia Faculty of Information Technology Thanh Do University Kim Chung Hoai Duc Ha Noi 100000 Viet Nam Department of Signal Processing and Communications Universidad Rey Juan Carlos Fuenlabrada 28942 Madrid Spain Science Economics and Insights Division Department of Climate Change Energy the Environment and Water NSW Government Sydney 2141 New South Wales Australia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares 28805 Madrid Spain

Electricity price ( E P ) forecasting is vital for effective market operation, strategic planning, and risk management in deregulated energy systems. However, the inherent volatility and complexity of electricity prices, shaped by demand supply dynamics, weather variability, and regulatory interventions, pose substantial challenges to accurate prediction. This study introduces a novel hybrid framework designed to improve forecasting accuracy by leveraging both signal decomposition and deep learning techniques. Specifically, the method integrates Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) for noise reduction and feature extraction, followed by a Multi Resolution Convolution (MRC) layer and a Bidirectional Long Short Term Memory (BiLSTM) network to capture multiscale temporal patterns in electricity price data. The model is applied to half hourly electricity price data from South Australia spanning January 2018 to December 2022. Its performance is benchmarked against a suite of traditional and hybrid models using a comprehensive set of twelve evaluation metrics. The results reveal that the proposed hybrid model consistently outperforms all baselines across seasons and forecast horizons. Notably, during the spring period, it achieved a Normalized Root Mean Square Error of ≈ 4.87 % , a Mean Absolute Percentage Error of ≈ 12.09 % , and a Global Performance Index of ≈ 3.22 . These improvements demonstrate the model’s ability to effectively handle the non-linear and nonstationary nature of E P . Overall, the findings underscore the potential of combining advanced decomposition methods with deep learning architectures to deliver more accurate and robust E P forecasts, thereby offering valuable support for decision making in complex and evolving energy markets.

关键词： 0000 1111 0000 1111 Deep learning Convolutional neural network Variational mode decomposition Empirical wavelet transform Residual connection Bayesian optimization

An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication

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Informatics in Medicine Unlocked 2023年 43卷

作者： MacMahon, Méabh Hwang, Woochang Yim, Soorin MacMahon, Eoghan Abraham, Alexandre Barton, Justin Tharmakulasingam, Mukunthan Bilokon, Paul Gaddi, Vasanthi Priyadarshini Han, Namshik Milner Therapeutics Institute University of Cambridge Cambridge United Kingdom Centre for Therapeutics Discovery LifeArc Stevenage United Kingdom Department of Bio and Brain Engineering KAIST Daejeon South Korea UCD School of Chemistry University College Dublin Dublin Ireland Implicity Paris France Institute of Structural and Molecular Biology Birkbeck University of London London United Kingdom Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Department of Computing Imperial College London London United Kingdom Department of Mathematics Imperial College London London United Kingdom Thalesians Ltd London United Kingdom Centre for Genetics and Genomics Versus Arthritis Division of Musculoskeletal and Dermatological Sciences School of Biological Sciences Faculty of Biology Medicine and Health The University of Manchester Manchester United Kingdom Cambridge Centre for AI in Medicine Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom

Background: Drug repurposing provides an opportunity to redeploy drugs, which are already tested for safety or approved for use in humans, for the treatment of diseases distinct from their primary indication. For example, the repurposing of dexamethasone and baricitinib has played a crucial role in saving patient lives during the SARS-CoV-2 pandemic. There remains a need to expand therapeutic approaches to prevent life-threatening complications in vulnerable patients with COVID-19. Method: Using an in silico approach based on structural similarity to drugs already in clinical trials for COVID-19, potential drugs were predicted for repurposing. For a subset of identified drugs with different targets to their corresponding COVID-19 clinical trial drug, a mechanism of action analysis based on affected pathways in the context of the first 24 h of infection was applied to establish whether they might have a role in inhibiting the replication of SARS-CoV-2. Results: Twenty-nine structurally similar drugs with potential for repurposing against COVID-19 were predicted. Two of these with the potential to inhibit SARS-CoV-2 replication were identified using mechanism of action analysis. Triamcinolone is a corticosteroid that is structurally similar to dexamethasone;gallopamil is a calcium channel blocker that is structurally similar to verapamil. Conclusion: The identification of these drugs as potentially useful for patients with COVID-19 who are at a higher risk of developing severe disease supports the use of in silico approaches to facilitate quick and cost-effective identification of drugs for repurposing. Such drugs could expand the number of treatments available to the subset of patients who are not fully protected by vaccination. © 2023 The Authors

关键词： Artificial neural network COVID-19 Drug mechanism Drug repurposing Gallopamil Triamcinolone Virus replication

Interactive virtual sound source for auditory localization tests

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Interactive virtual sound source for auditory localization t...

2018 Baltic URSI Symposium, URSI 2018

作者： Meyer, Andrzej Pawlowski, Pawel Dabrowski, Adam Loba, Wawrzyniec Hojan-Jezierska, Dorota Kubisz, Leszek Division of Signal Processing and Electronic Systems Faculty of Computing Poznan University of Technology Poland Department of Hearing Healthcare Profession Department of Biophysics Poznan University of Medical Sciences Poland

ISBN: (纸本)9788394942137

The paper presents a prototype of an interactive virtual sound source for auditory localization tests. Instead of mechanical movement of a speaker, the position of the sound source is virtually defined via digital signal processing. The virtual source can be placed in any angle in the range of 0 to 180 degrees, while the sound is generated via 5-channel audio system. The additional interactive feedback channel, with easy controlled pointer on a LED tape, supports controlling the source and giving responses by the examined person. The proposed method of the virtual sound source makes the system faster, cheaper, and easier to control in comparison to the classical (moving sound source) solutions. Preliminary tests confirmed achievement of the design assumptions. © 2018 Warsaw University of Technology.

关键词： Digital signal processing

Method of adaptive pixel averaging for impulse noise reduction in digital images

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Method of adaptive pixel averaging for impulse noise reducti...

2018 Baltic URSI Symposium, URSI 2018

作者： Konieczka, Adam Balcerek, Julian Dabrowski, Adam Poznan University of Technology Faculty of Computing Institute of Automation and Robotics Division of Signal Processing and Electronic Systems ul. Jana Pawla II 24 Poznan60-965 Poland

ISBN: (纸本)9788394942137

In this paper a new nonlinear method for salt and pepper and random-valued impulse noise reduction in digital images is proposed. The authors achieved high effectiveness combined with a low computational complexity. The proposed solution is based on iterative process of noisy pixels detection using noise profiles and replacement of these pixels using the average values of neighboring noise free pixels. Noise profiles are trained using artificially noised test images. Experiments were performed on standard test images in order to compare proposed approach with other popular techniques. A quality measurement was the peak signal-to-noise ratio (PSNR). In many instances the proposed method was better than other existing techniques of impulse noise reduction. © 2018 Warsaw University of Technology.

关键词： Image reconstruction

Understanding metric-related pitfalls in image analysis validation

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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

An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication

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arXiv 2021年

作者： MacMahon, Méabh Hwang, Woochang Yim, Soorin MacMahon, Eoghan Abraham, Alexandre Barton, Justin Tharmakulasingam, Mukunthan Bilokon, Paul Gaddi, Vasanthi Priyadarshini Han, Namshik Milner Therapeutics Institute University of Cambridge Cambridge United Kingdom Centre for Therapeutics Discovery LifeArc Stevenage United Kingdom Department of Bio and Brain Engineering KAIST Daejeon Korea Republic of UCD School of Chemistry University College Dublin Dublin Ireland Dataiku Paris France Institute of Structural and Molecular Biology Birkbeck University of London London United Kingdom Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Department of Computing Imperial College London London United Kingdom Department of Mathematics Imperial College London London United Kingdom Thalesians Ltd London United Kingdom Centre for Genetics and Genomics Versus Arthritis Division of Musculoskeletal and Dermatological Sciences School of Biological Sciences Faculty of Biology Medicine and Health The University of Manchester Manchester United Kingdom Cambridge Centre for AI in Medicine Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom

Drug repurposing provides an opportunity to redeploy drugs, which ideally are already approved for use in humans, for the treatment of other diseases. For example, the repurposing of dexamethasone and baricitinib has played a crucial role in saving patient lives during the ongoing SARS-CoV-2 pandemic. There remains a need to expand therapeutic approaches to prevent life-threatening complications in patients with COVID-19. Using an in silico approach based on structural similarity to drugs already in clinical trials for COVID-19, potential drugs were predicted for repurposing. For a subset of identified drugs with different targets to their corresponding COVID-19 clinical trial drug, a mechanism of action analysis was applied to establish whether they might have a role in inhibiting the replication of SARS-CoV-2. Of sixty drugs predicted in this study, two with the potential to inhibit SARS-CoV-2 replication were identified using mechanism of action analysis. Triamcinolone is a corticosteroid that is structurally similar to dexamethasone;gallopamil is a calcium channel blocker that is structurally similar to verapamil. In silico approaches indicate possible mechanisms of action for both drugs in inhibiting SARS-CoV-2 replication. The identification of these drugs as potentially useful for patients with COVID-19 who are at a higher risk of developing severe disease supports the use of in silico approaches to facilitate quick and cost-effective drug repurposing. Such drugs could expand the number of treatments available to patients who are not protected by vaccination. © 2021, CC BY-NC-ND.

关键词： Coronavirus

Screening and evaluation of potential clinically significant HIV drug combinations against SARS-CoV-2 virus

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arXiv 2020年

作者： Tomić, Draško Skala, Karolj Szasz, Attila Marcel Rezeli, Melinda Vrca, Vesna Bačić Pirkić, Boris Petrik, Jozsef Janđel, Vladimir Periša, Marija Milković Rogina, Branka Medved Mesarić, Josip Davidović, Davor Lipić, Tomislav Center for Informatics and Computing Ruđer Bošković Institute Zagreb Croatia Department of Bioinformatics and Cancer Center Semmelweis University Budapest Hungary Department of Biomedical Engineering Lund University Lund Sweden Central Hospital Pharmacy Clinical Hospital Center Dubrava Zagreb Croatia Surgery Orthopedics and Ophthalmology Clinic Faculty of Veterinary Medicine University of Zagreb Zagreb Croatia Department of Medical Biochemistry and Hematology Faculty of Pharmacy and Biochemistry University of Zagreb Zagreb Croatia Deprtment of Obstetricy Clinical Hospital Centre Zagreb Croatia Institute of Pathology School of Medicine University of Zagreb Zagreb Croatia Laboratory for Information and Signal Processing Division of Electronics Ruđer Bosković Institute Zagreb Croatia Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

In this study, we investigated the inhibition of SARS-CoV-2 spike glycoprotein with HIV drugs and their combinations. This glycoprotein is essential for the reproduction of the SARS-COV-2 virus, so its inhibition opens new avenues for the treatment of patients with COVID-19 disease. In doing so, we used the VINI in silico model of cancer, whose high accuracy in finding effective drugs and their combinations was confirmed in vitro by comparison with existing results from NCI-60 bases, and in vivo by comparison with existing clinical trial results. In the first step, the VINI model calculated the inhibition efficiency of SARS-CoV-2 spike glycoprotein with 44 FDA-approved antiviral drugs. Of these drugs, HIV drugs have been shown to be effective, while others mainly have shown weak or no efficiency. Subsequently, the VINI model calculated the inhibition efficiency of all possible double and triple HIV drug combinations, and among them identified ten with the highest inhibition efficiency. These ten combinations were analyzed by Medscape drug-drug interaction software and LexiComp Drug Interactions. All combinations except the combination of cobicistat_abacavir_rilpivirine appear to have serious interactions (risk rating category D) when dosage adjustments/reductions are required for possible toxicity. Finally, the VINI model compared the inhibition efficiency of cobicistat_abacivir_rilpivirine combination with cocktails and individual drugs already used or planned to be tested against SARS-CoV-2. Combination cobicistat_abacivir_rilpivirine demonstrated the highest inhibition of SARS-CoV-2 spike glycoprotein over others. Thus, this combination seems to be a promising candidate for the further in vitro testing and clinical trials. Copyright © 2020, The Authors. All rights reserved.

关键词： SARS

Metrics Reloaded: Recommendations for image analysis validation

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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