Institution
Katholieke Universiteit Leuven
Education•Leuven, Belgium•
About: Katholieke Universiteit Leuven is a education organization based out in Leuven, Belgium. It is known for research contribution in the topics: Population & Transplantation. The organization has 61109 authors who have published 176584 publications receiving 6210872 citations.
Topics: Population, Transplantation, CMOS, European union, Stars
Papers published on a yearly basis
Papers
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TL;DR: The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
Abstract: The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.
This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
15,935 citations
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07 May 2006TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Abstract: In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.
13,011 citations
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TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
12,449 citations
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University of Chicago1, University of Padua2, McGill University3, Johns Hopkins University4, French Institute of Health and Medical Research5, Uppsala University6, University of California, San Francisco7, MedStar Washington Hospital Center8, Katholieke Universiteit Leuven9, University of Liège10, Harvard University11, Ghent University Hospital12, University of Toronto13
TL;DR: This document provides updated normal values for all four cardiac chambers, including three-dimensional echocardiography and myocardial deformation, when possible, on the basis of considerably larger numbers of normal subjects, compiled from multiple databases.
Abstract: The rapid technological developments of the past decade and the changes in echocardiographic practice brought about by these developments have resulted in the need for updated recommendations to the previously published guidelines for cardiac chamber quantification, which was the goal of the joint writing group assembled by the American Society of Echocardiography and the European Association of Cardiovascular Imaging. This document provides updated normal values for all four cardiac chambers, including three-dimensional echocardiography and myocardial deformation, when possible, on the basis of considerably larger numbers of normal subjects, compiled from multiple databases. In addition, this document attempts to eliminate several minor discrepancies that existed between previously published guidelines.
11,568 citations
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TL;DR: The metafor package provides functions for conducting meta-analyses in R and includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models.
Abstract: The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. Meta-regression analyses with continuous and categorical moderators can be conducted in this way. Functions for the Mantel-Haenszel and Peto's one-step method for meta-analyses of 2 x 2 table data are also available. Finally, the package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias.
11,237 citations
Authors
Showing all 61602 results
Name | H-index | Papers | Citations |
---|---|---|---|
Paul Elliott | 153 | 773 | 103839 |
Bart Staels | 152 | 824 | 86638 |
Christopher P. Cannon | 151 | 1118 | 108906 |
Christopher M. Dobson | 150 | 1008 | 105475 |
Vivek Sharma | 150 | 3030 | 136228 |
Kypros H. Nicolaides | 147 | 1302 | 87091 |
Hugh A. Sampson | 147 | 816 | 76492 |
Jean-Frederic Colombel | 147 | 1125 | 98944 |
Børge G. Nordestgaard | 147 | 1047 | 95530 |
Jean Bousquet | 145 | 1288 | 96769 |
Ruth J. F. Loos | 142 | 647 | 92485 |
Michael J. Keating | 140 | 1169 | 76353 |
Wilmar B. Schaufeli | 137 | 513 | 95718 |
Georgios B. Giannakis | 137 | 1321 | 73517 |
Keith A.A. Fox | 136 | 830 | 95960 |