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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 image. 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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Human detection in semi-dense scenes using HOG descriptor and mixture of SVMs

Human detection in semi-dense scenes using HOG descriptor an...

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3rd International Conference on computer and Knowledge engineering, ICCKE 2013

作者： Rajaei, Afsane Shayegh, Hamidreza Moghaddam Charkari, Nasrollah Parallel Processing Image. Computer Engineering Department Electrical and Computer Faculty Tarbiat Modares Tehran Iran

ISBN: (纸本)9781479920921

Human detection has recently received significant attention in the field of computer vision. Accurate detection of human bodies is an essential component required by a variety of applications such as automated surveillance, advanced user interface and sport analysis. In this paper, we present a new method for human detection in video frames, using learning algorithm, feature extraction and sliding window. People detection is a challenging topic, especially in complex scenes when occlusion occurs. In this paper a new technique based on the body part detection and HOG (Histogram of Gradient) features is proposed for human detection with occlusions. Proposed HOG descriptor provides lower feature vector length in comparison with state of the art methods approaches. Using human detectors alone, cause to high frequent false positive. Thus, we propose a novel classifier-fusion learning algorithm, instead of single classifier. The experimental results show that the new proposed method provides lower false positive and raise higher precision and recall in comparison with state of the art methods. © 2013 IEEE.

关键词： Learning algorithms

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Application of QR decomposition-based least squares technique to improve air traffic control radar

Application of QR decomposition-based least squares techniqu...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： T. Ogunfunmi Nam Ling Signal and Image Processing Laboratory (SIPL) Department of Electrical Engineering Santa Clara University Santa Clara CA USA Computer Systems and Parallel Processing Research Laboratory (CSPPRL) Department of Computer Engineering Santa Clara University Santa Clara CA USA

For real-time radar processing, it is very desirable to have an algorithm that does not assume restricted statistics of the input data and can be implemented for high-speed processing (without a high cost) to meet real-time requirements. We therefore apply the QR decomposition-based least-squares method for linear prediction to the problem of computing the reflection coefficients of a lattice predictor, instead of using the conventional Burg algorithm. We also propose a modified one-dimensional ring architecture for implementing the QR method of least-squares. The particular application considered in this case is that of surveillance radar systems for air traffic control.< >

关键词： Least squares methods Air traffic control Radar applications Signal processing algorithms Radar signal processing Statistics Systolic arrays Radar imaging Signal processing Surveillance

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