Institution
ETH Zurich
Education•Zurich, Switzerland•
About: ETH Zurich is a(n) education organization based out in Zurich, Switzerland. It is known for research contribution in the topic(s): Population & Galaxy. The organization has 48393 authors who have published 122408 publication(s) receiving 5111383 citation(s). The organization is also known as: Swiss Federal Institute of Technology in Zurich & Eidgenössische Technische Hochschule Zürich.
Topics: Population, Galaxy, Laser, Catalysis, Large Hadron Collider
Papers published on a yearly basis
Papers
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TL;DR: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis that facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system.
Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
30,888 citations
University of Udine1, International School for Advanced Studies2, National Research Council3, Massachusetts Institute of Technology4, University of Paris5, Princeton University6, University of Minnesota7, ParisTech8, University of Milan9, International Centre for Theoretical Physics10, University of Paderborn11, ETH Zurich12, École Polytechnique Fédérale de Lausanne13
Abstract: QUANTUM ESPRESSO is an integrated suite of computer codes for electronic-structure calculations and materials modeling, based on density-functional theory, plane waves, and pseudopotentials (norm-conserving, ultrasoft, and projector-augmented wave). The acronym ESPRESSO stands for opEn Source Package for Research in Electronic Structure, Simulation, and Optimization. It is freely available to researchers around the world under the terms of the GNU General Public License. QUANTUM ESPRESSO builds upon newly-restructured electronic-structure codes that have been developed and tested by some of the original authors of novel electronic-structure algorithms and applied in the last twenty years by some of the leading materials modeling groups worldwide. Innovation and efficiency are still its main focus, with special attention paid to massively parallel architectures, and a great effort being devoted to user friendliness. QUANTUM ESPRESSO is evolving towards a distribution of independent and interoperable codes in the spirit of an open-source project, where researchers active in the field of electronic-structure calculations are encouraged to participate in the project by contributing their own codes or by implementing their own ideas into existing codes.
15,767 citations
Queen's University Belfast1, Collège de France2, English Heritage3, University of Arizona4, University of Sheffield5, University of Oxford6, University of Minnesota7, University of Hohenheim8, University of Kiel9, Lawrence Livermore National Laboratory10, University of Bergen11, ETH Zurich12, University of Waikato13, Woods Hole Oceanographic Institution14, Swiss Federal Institute for Forest, Snow and Landscape Research15, Cornell University16, University of Bristol17, University of Glasgow18, University of California, Irvine19, University of New South Wales20
Abstract: Additional co-authors: TJ Heaton, AG Hogg, KA Hughen, KF Kaiser, B Kromer, SW Manning, RW Reimer, DA Richards, JR Southon, S Talamo, CSM Turney, J van der Plicht, CE Weyhenmeyer
13,118 citations
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: 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
Authors
Showing all 48393 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ralph Weissleder | 184 | 1160 | 142508 |
Ruedi Aebersold | 182 | 879 | 141881 |
David L. Kaplan | 177 | 1944 | 146082 |
Andrea Bocci | 172 | 2402 | 176461 |
Richard H. Friend | 169 | 1182 | 140032 |
Lorenzo Bianchini | 152 | 1516 | 106970 |
David D'Enterria | 150 | 1592 | 116210 |
Andreas Pfeiffer | 149 | 1756 | 131080 |
Bernhard Schölkopf | 148 | 1092 | 149492 |
Martin J. Blaser | 147 | 820 | 104104 |
Sebastian Thrun | 146 | 434 | 98124 |
Antonio Lanzavecchia | 145 | 408 | 100065 |
Christoph Grab | 144 | 1359 | 144174 |
Kurt Wüthrich | 143 | 739 | 103253 |
Maurizio Pierini | 143 | 1782 | 104406 |