scispace - formally typeset

Institution

ETH Zurich

EducationZurich, 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.


Papers
More filters
Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

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.

12,404 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.
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

NameH-indexPapersCitations
Ralph Weissleder1841160142508
Ruedi Aebersold182879141881
David L. Kaplan1771944146082
Andrea Bocci1722402176461
Richard H. Friend1691182140032
Lorenzo Bianchini1521516106970
David D'Enterria1501592116210
Andreas Pfeiffer1491756131080
Bernhard Schölkopf1481092149492
Martin J. Blaser147820104104
Sebastian Thrun14643498124
Antonio Lanzavecchia145408100065
Christoph Grab1441359144174
Kurt Wüthrich143739103253
Maurizio Pierini1431782104406
Network Information
Related Institutions (5)
Massachusetts Institute of Technology

268K papers, 18.2M citations

96% related

University of Maryland, College Park

155.9K papers, 7.2M citations

95% related

Centre national de la recherche scientifique

382.4K papers, 13.6M citations

95% related

University of California, Berkeley

265.6K papers, 16.8M citations

94% related

Princeton University

146.7K papers, 9.1M citations

94% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2022175
20218,519
20208,657
20197,882
20187,453
20177,416