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Institution

Wuhan University

EducationWuhan, China
About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Population & Feature extraction. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.


Papers
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Proceedings ArticleDOI
18 Jun 2018
TL;DR: The proposed method named Rotation-sensitive Regression Detector (RRD) achieves state-of-the-art performance on several oriented scene text benchmark datasets, including ICDAR 2015, MSRA-TD500, RCTW-17, and COCO-Text, and achieves a significant improvement on a ship collection dataset, demonstrating its generality on oriented object detection.
Abstract: Text in natural images is of arbitrary orientations, requiring detection in terms of oriented bounding boxes. Normally, a multi-oriented text detector often involves two key tasks: 1) text presence detection, which is a classification problem disregarding text orientation; 2) oriented bounding box regression, which concerns about text orientation. Previous methods rely on shared features for both tasks, resulting in degraded performance due to the incompatibility of the two tasks. To address this issue, we propose to perform classification and regression on features of different characteristics, extracted by two network branches of different designs. Concretely, the regression branch extracts rotation-sensitive features by actively rotating the convolutional filters, while the classification branch extracts rotation-invariant features by pooling the rotation-sensitive features. The proposed method named Rotation-sensitive Regression Detector (RRD) achieves state-of-the-art performance on several oriented scene text benchmark datasets, including ICDAR 2015, MSRA-TD500, RCTW-17, and COCO-Text. Furthermore, RRD achieves a significant improvement on a ship collection dataset, demonstrating its generality on oriented object detection.

415 citations

Journal ArticleDOI
TL;DR: The data support those described by others that COVID-19 infection results from human-to-human transmission, including familial clustering of cases, and nosocomial transmission.
Abstract: BACKGROUND: In December 2019, a series of pneumonia cases of unknown cause emerged in Wuhan, Hubei, China. In this study, we investigate the clinical and laboratory features and short-term outcomes of patients with coronavirus disease 2019 (COVID-19). METHODS: All patients with COVID-19 admitted to Wuhan University Zhongnan Hospital in Wuhan, China, between 3 January and 1 February 2020 were included. All those patients were with laboratory-confirmed infections. Epidemiological, clinical, and radiological characteristics; underlying diseases; laboratory tests; treatments; complications; and outcomes data were collected. Outcomes were followed up at discharge until 15 February 2020. RESULTS: The study cohort included 102 adult patients. The median age was 54 years (interquartile ranger, 37-67 years), and 48.0% were female. A total of 34 patients (33.3%) were exposed to a source of transmission in the hospital setting (as health-care workers, patients, or visitors) and 10 patients (9.8%) had a familial cluster. There were 18 patients (17.6%) who were admitted to the intensive care unit (ICU), and 17 patients died (mortality, 16.7%; 95% confidence interval, 9.4-23.9%). Those patients who survived were younger, were more likely to be health-care workers, and were less likely to suffer from comorbidities. They were also less likely to suffer from complications. There was no difference in drug treatment rates between the survival and nonsurvival groups. Those patients who survived were less likely to require admission to the ICU (14.1% vs 35.3% of those admitted). Chest imaging examinations showed that patients who died were more likely to have ground-glass opacity (41.2% vs 12.9% in survivors). CONCLUSIONS: The mortality rate was high among the COVID-19 patients described in our cohort who met our criteria for inclusion in this analysis. The patient characteristics seen more frequently in those who died were the development of systemic complications following onset of the illness and a severity of disease requiring admission to the ICU. Our data support those described by others indicating that COVID-19 infection results from human-to-human transmission, including familial clustering of cases, and from nosocomial transmission. There were no differences in mortality among those who did or did not receive antimicrobial or glucocorticoid drug treatments.

414 citations

Journal ArticleDOI
Yong Hu1, Jiazhong Sun1, Zhe Dai1, Haohua Deng1, Xin Li1, Qi Huang1, Yuwen Wu1, Li Sun1, Yancheng Xu1 
TL;DR: A meta-analysis of clinical and epidemiological studies on confirmed cases of COVID-19 found the case severe rate and mortality is lower than that of SARS and MERS, and the most prevalent comorbidities are hypertension and diabetes which are associated with the severity of CO VID-19.

413 citations

Journal ArticleDOI
TL;DR: In this work, a novel approach was developed to prepare an engineered biochar from KMnO4 treated hickory wood through slow pyrolysis (600°C) that had strong sorption ability and the removal of the heavy metals by the biochars was mainly through surface adsorption mechanisms involving both the surface MnOx particles and oxygen-containing groups.

413 citations

Journal ArticleDOI
TL;DR: A new multifeature model, aiming to construct a support vector machine (SVM) ensemble combining multiple spectral and spatial features at both pixel and object levels is proposed, which provides more accurate classification results compared to the voting and probabilistic models.
Abstract: In recent years, the resolution of remotely sensed imagery has become increasingly high in both the spectral and spatial domains, which simultaneously provides more plentiful spectral and spatial information. Accordingly, the accurate interpretation of high-resolution imagery depends on effective integration of the spectral, structural and semantic features contained in the images. In this paper, we propose a new multifeature model, aiming to construct a support vector machine (SVM) ensemble combining multiple spectral and spatial features at both pixel and object levels. The features employed in this study include a gray-level co-occurrence matrix, differential morphological profiles, and an urban complexity index. Subsequently, three algorithms are proposed to integrate the multifeature SVMs: certainty voting, probabilistic fusion, and an object-based semantic approach, respectively. The proposed algorithms are compared with other multifeature SVM methods including the vector stacking, feature selection, and composite kernels. Experiments are conducted on the hyperspectral digital imagery collection experiment DC Mall data set and two WorldView-2 data sets. It is found that the multifeature model with semantic-based postprocessing provides more accurate classification results (an accuracy improvement of 1-4% for the three experimental data sets) compared to the voting and probabilistic models.

408 citations


Authors

Showing all 93441 results

NameH-indexPapersCitations
Jing Wang1844046202769
Jiaguo Yu178730113300
Lei Jiang1702244135205
Gang Chen1673372149819
Omar M. Yaghi165459163918
Xiang Zhang1541733117576
Yi Yang143245692268
Thomas P. Russell141101280055
Jun Chen136185677368
Lei Zhang135224099365
Chuan He13058466438
Han Zhang13097058863
Lei Zhang130231286950
Zhen Li127171271351
Chao Zhang127311984711
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023286
20221,139
20219,716
20209,672
20197,977
20186,629