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arXiv 2023年

作者： Li, Chong Fan, Zhun Chen, Ying Lin, Huibiao Moretti, Laura Loprencipe, Giuseppe Sheng, Weihua Wang, Kelvin C.P. The Key Lab of Digital Signal and Image Processing of Guangdong Province College of Engineering Shantou University Shantou515063 China The Department of Civil Construction and Environmental Engineering Sapienza University of Rome Rome00184 Italy The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States The School of Civil and Environmental Engineering Oklahoma State University StillwaterOK74078 United States

Automatic pavement crack detection is an important task to ensure the functional performances of pavements during their service life. Inspired by deep learning (DL), the encoder-decoder framework is a powerful tool for crack detection. However, these models are usually open-loop (OL) systems that tend to treat thin cracks as the background. Meanwhile, these models can not automatically correct errors in the prediction, nor can it adapt to the changes of the environment to automatically extract and detect thin cracks. To tackle this problem, we embed closed-loop feedback (CLF) into the neural network so that the model could learn to correct errors on its own, based on generative adversarial networks (GAN). The resulting model is called CrackCLF and includes the front and back ends, i.e. segmentation and adversarial network. The front end with U-shape framework is employed to generate crack maps, and the back end with a multi-scale loss function is used to correct higher-order inconsistencies between labels and crack maps (generated by the front end) to address open-loop system issues. Empirical results show that the proposed CrackCLF outperforms others methods on three public datasets. Moreover, the proposed CLF can be defined as a plug and play module, which can be embedded into different neural network models to improve their performances. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Generation of Synthetic Echocardiograms Using Video Diffusion Models

Generation of Synthetic Echocardiograms Using Video Diffusio...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Alexandre Olive Pellicer Amit Kumar Singh Yadav Kratika Bhagtani Ziyue Xiang Zygmunt Pizlo Irmina Gradus-Pizlo Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Department of Cognitive Sciences University of California-Irvine Irvine California USA School of Medicine University of California-Irvine Irvine California USA Escola Tècnica Superior d’Enginyeria de Telecomunicació de Barcelona Universitat Politècnica de Catalunya (UPC) Barcelona Spain

An echocardiogram is a video sequence of a human heart captured using ultrasound imaging, which helps in diagnosis of cardiovascular diseases. Deep learning methods, which require large amounts of training data, have shown success in using echocardiograms to detect cardiovascular disorders. Large datasets of echocardiograms that can be used for machine learning training are scarce. This problem can be addressed by generating synthetic echocardiograms that can be used for machine learning training. In this paper, we propose a video diffusion method for echocardiograms generation. We show that our method generates better echocardiograms with higher resolution as compared to existing methods.

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EU COST ACTION ON FUTURE GENERATION OPTICAL WIRELESS COMMUNICATION TECHNOLOGIES -NEWFOCUS CA19111 A White Paper

arXiv

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arXiv 2022年

作者： Ghassemlooy, Z. Khalighi, M. Ali Zvanovec, S. Stevens, N. Alves, L.N. Shrestha, A. Uysal, Murat Vegni, Anna Maria Diamantoulakis, Panagiotis D. Papanikolaou, Vasilis K. Karagiannidis, George K. Ortega, Beatriz Almenar, Vicenç Bouchet, Olivier Ladid, Latif Ozyegin University Turkey Department of Industrial Electrical and Mechanical Engineering Roma Tre University Italy Wireless Communications & Information Processing Group Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54 124 Greece Instituto de Telecomunicaciones y Aplicaciones Multimedia Universitat Politècnica deValència Camino de Vera s/n Valencia46022 Spain Orange Innovation Secan-Lab University of Luxembourg Luxembourg

The EU COST Action NEWFOCUS is focused on investigating radical solutions with the potential to impact the design of future wireless networks. It aims to address some of the challenges in OWC and establish it as an efficient technology that can satisfy the demanding requirements of backhaul and access network levels in 5G networks. This also includes the use of hybrid links that associate OWC with radiofrequency or wired/fiber-based technologies. The focus of this White Paper is on the use of optical wireless communication (OWC) as enabling technology in a range of areas outlined in HE's Pillar II including Health, Manufacturing, Intelligent Transportation Systems (ITS), Unmanned Aerial Vehicles and Network and Protocol. © 2022, CC BY-NC-ND.

关键词： 5G mobile communication systems

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Discrete, recurrent, and scalable patterns in human judgement underlie affective picture ratings

arXiv

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arXiv 2022年

作者： Azcona, Emanuel A. Kim, Byoung-Woo Vike, Nicole L. Bari, Sumra Lalvani, Shamal Stefanopoulos, Leandros Woodward, Sean Block, Martin Katsaggelos, Aggelos K. Breiter, Hans C. Image and Video Processing Lab Department of Electrical and Computer Engineering Northwestern University EvanstonIL United States Department of Psychiatry and Behavioral Sciences Northwestern University ChicagoIL United States Integrated Marketing Communications Northwestern University EvanstonIL United States Department of Computer Science Northwestern University EvanstonIL United States Department of Radiology Northwestern University ChicagoIL United States Laboratory of Neuroimaging and Genetics Department of Psychiatry Massachusetts General Hospital Harvard School of Medicine BostonMA United States

Operant keypress tasks, where each action has a consequence, have been analogized to the construct of "wanting" and produce lawful relationships in humans that quantify preferences for approach and avoidance behavior. It is unknown if rating tasks without an operant framework, which can be analogized to "liking", show similar lawful relationships. We studied three independent cohorts of participants (N = 501, 506, and 4,019 participants) collected by two distinct organizations, using the same 7-point Likert scale to rate negative to positive preferences for pictures from the International Affective Picture Set. Picture ratings without an operant framework produced similar value functions, limit functions, and tradeoff functions to those reported in the literature for operant keypress tasks, all with Rn goodness of fits above 0.75. These value, limit, and trade-off functions were discrete in their mathematical formulation, recurrent across all three independent cohorts, and demonstrated scaling between individual and group curves. In all three experiments, the computation of loss aversion showed 95% confidence intervals below the value of 2, arguing against a strong overweighting of losses relative to gains, as has previously been reported for keypress tasks or games of chance with calibrated uncertainty. Graphed features from the three cohorts were similar and argue that preference assessments meet three of four criteria for lawfulness, providing a simple, short, and low-cost method for the quantitative assessment of preference without forced choice decisions, games of chance, or operant keypressing. This approach can easily be implemented on any digital device with a screen (e.g., cellphones). © 2022, CC BY.

关键词： Big data

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Data and information quality assessment in a possibilistic framework based on the Choquet Integral

Data and information quality assessment in a possibilistic f...

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International Conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Sonda Ammar Bouhamed Hatem Dardouri Imene Khanfir Kallel Eloi Bossé Basel Solaiman Control and Energy Managment (CEM Lab) Sfax Tunisia Image and Information Processing Department (iTi) IMT-Atlantique Brest France Expertises Parafuse and Electrical and Computer Engineering McMaster University Canada

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

Designing methods for assessment of data and information quality is a relatively new and rather difficult problem. This paper presents a new approach for data and information quality assessment in the possibilistic framework based on Choquet Integral. The aim is not only to estimate data or information quality but also to differentiate between two quality degrees that are very close. The methodology of the Choquet integral is extended to the possibilistic framework. The proposed approach is validated using both: synthetic data and benchmark datasets. The experimental results clearly show that the proposed approach is able to assess the quality of the considered data and information.

关键词： Uncertainty Quality assessment Measurement uncertainty Benchmark testing Mathematical model Glass Possibility theory

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Hybrid attention based multimodal network for spoken language classification 27

Hybrid attention based multimodal network for spoken languag...

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27th International Conference on Computational Linguistics, COLING 2018

作者： Gu, Yue Yang, Kangning Fu, Shiyu Chen, Shuhong Li, Xinyu Marsic, Ivan Multimedia Image Processing Lab Electrical and Computer Engineering Department Rutgers University PiscatawayNJ United States

ISBN: (纸本)9781948087506

We examine the utility of linguistic content and vocal characteristics for multimodal deep learning in human spoken language understanding. We present a deep multimodal network with both feature attention and modality attention to classify utterance-level speech data. The proposed hybrid attention architecture helps the system focus on learning informative representations for both modality-specific feature extraction and model fusion. The experimental results show that our system achieves state-of-the-art or competitive results on three published multimodal datasets. We also demonstrated the effectiveness and generalization of our system on a medical speech dataset from an actual trauma scenario. Furthermore, we provided a detailed comparison and analysis of traditional approaches and deep learning methods on both feature extraction and fusion. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Feature extraction

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QUBIQ: Uncertainty Quantification for Biomedical image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： image segmentation

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Single noisy image super resolution by minimizing nuclear norm in virtual sparse domain 6th

Single noisy image super resolution by minimizing nuclear no...

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6th National Conference on computer Vision, Pattern Recognition, image processing and Graphics, NCVPRIPG 2017

作者： Mandal, Srimanta Rajagopalan, A.N. Image Processing and Computer Vision Lab Department of Electrical Engineering IIT Madras Chennai600036 India

ISBN: (纸本)9789811300196

Super-resolving a noisy image is a challenging problem, and needs special care as compared to the conventional super resolution approaches, when the power of noise is unknown. In this scenario, we propose an approach to super-resolve single noisy image by minimizing nuclear norm in a virtual sparse domain that tunes with the power of noise via parameter learning. The approach minimizes nuclear norm to explore the inherent low-rank structure of visual data, and is further augmented with coarse-to-fine information by adaptively re-aligning the data along the principal components of a dictionary in virtual sparse domain. The experimental results demonstrate the robustness of our approach across different powers of noise. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Optical resolving power

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AIM 2020 Challenge on image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European Conference on computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

arXiv

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arXiv 2020年

作者： Azcona, Emanuel Besson, Pierre Wu, Yunan Punjabi, Arjun Martersteck, Adam Dravid, Amil Parrish, Todd B. Bandt, S. Kathleen Katsaggelos, Aggelos K. Image and Video Processing Laboratory Department of Electrical and Computer Engineering Northwestern University IL United States Lab Northwestern Memorial Hospital IL United States Neuroimaging Laboratory Department of Radiology Northwestern University IL United States Augmented Intelligence in Medical Imaging Northwestern University IL United States

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type. Copyright © 2020, The Authors. All rights reserved.

关键词： Mesh generation

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