Institution
Katholieke Universiteit Leuven
Education•Leuven, Belgium•
About: Katholieke Universiteit Leuven is a(n) education organization based out in Leuven, Belgium. It is known for research contribution in the topic(s): Population & Transplantation. The organization has 61109 authors who have published 176584 publication(s) receiving 6210872 citation(s).
Topics: Population, Transplantation, CMOS, European union, Stars
Papers published on a yearly basis
Papers
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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.
12,404 citations
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.
11,545 citations
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.
Abstract: This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF 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 (specifically, 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 encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters. We conclude the article with SURF's application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF's usefulness in a broad range of topics in computer vision.
11,276 citations
TL;DR: 2007 Guidelines for the Management of Arterial Hypertension : The Task Force for the management of Arterspertension of the European Society ofhypertension (ESH) and of theEuropean Society of Cardiology (ESC).
Abstract: 2007 Guidelines for the Management of Arterial Hypertension : The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC).
9,819 citations
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.
8,690 citations
Authors
Showing all 61108 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eugene Braunwald | 230 | 1711 | 264576 |
Joseph L. Goldstein | 207 | 556 | 149527 |
Rakesh K. Jain | 200 | 1467 | 177727 |
Stefan Schreiber | 178 | 1233 | 138528 |
Masayuki Yamamoto | 171 | 1576 | 123028 |
Jun Wang | 166 | 1093 | 141621 |
David R. Jacobs | 165 | 1262 | 113892 |
Klaus Müllen | 164 | 2125 | 140748 |
Peter Carmeliet | 164 | 844 | 122918 |
Hua Zhang | 163 | 1503 | 116769 |
William J. Sandborn | 162 | 1317 | 108564 |
Elliott M. Antman | 161 | 716 | 179462 |
Tobin J. Marks | 159 | 1621 | 111604 |
Ian A. Wilson | 158 | 971 | 98221 |
Johan Auwerx | 158 | 653 | 95779 |