scispace - formally typeset
Search or ask a question
Institution

Katholieke Universiteit Leuven

EducationLeuven, 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.


Papers
More filters
Journal ArticleDOI
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

Book ChapterDOI
07 May 2006
TL;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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Paul Elliott153773103839
Bart Staels15282486638
Christopher P. Cannon1511118108906
Christopher M. Dobson1501008105475
Vivek Sharma1503030136228
Kypros H. Nicolaides147130287091
Hugh A. Sampson14781676492
Jean-Frederic Colombel147112598944
Børge G. Nordestgaard147104795530
Jean Bousquet145128896769
Ruth J. F. Loos14264792485
Michael J. Keating140116976353
Wilmar B. Schaufeli13751395718
Georgios B. Giannakis137132173517
Keith A.A. Fox13683095960
Network Information
Related Institutions (5)
University of Minnesota
257.9K papers, 11.9M citations

94% related

University of Toronto
294.9K papers, 13.5M citations

93% related

University of California, San Diego
204.5K papers, 12.3M citations

93% related

Stanford University
320.3K papers, 21.8M citations

93% related

McGill University
162.5K papers, 6.9M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023307
2022857
202111,007
202010,541
20199,719
20189,532