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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.

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Topics: Population, Galaxy, Laser ...read more
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
Papers
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Open accessJournal ArticleDOI: 10.1038/NMETH.2019
01 Jul 2012-Nature Methods
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.

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Topics: Software design (51%), Software (50%)

30,888 Citations


Open accessJournal ArticleDOI: 10.1088/0953-8984/21/39/395502
Paolo Giannozzi1, Stefano Baroni2, Stefano Baroni3, Nicola Bonini4  +37 moreInstitutions (13)
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.

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Topics: Quantum ESPRESSO (66%), Espresso (56%)

15,767 Citations


Open accessJournal ArticleDOI: 10.2458/AZU_JS_RC.55.16947
Paula J. Reimer1, Edouard Bard2, Alex Bayliss3, J. Warren Beck4  +26 moreInstitutions (20)
01 Jan 2009-Radiocarbon
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

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13,118 Citations


Open accessBook ChapterDOI: 10.1007/11744023_32
Herbert Bay1, Tinne Tuytelaars2, Luc Van Gool1Institutions (2)
07 May 2006-
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.

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  • Fig. 1. Left to right: The (discretised and cropped) Gaussian second order partial derivatives in y-direction and xy-direction, and our approximations thereof using box filters. The grey regions are equal to zero.
    Fig. 1. Left to right: The (discretised and cropped) Gaussian second order partial derivatives in y-direction and xy-direction, and our approximations thereof using box filters. The grey regions are equal to zero.
  • Fig. 2. Left: Detected interest points for a Sunflower field. This kind of scenes shows clearly the nature of the features from Hessian-based detectors. Middle: Haar wavelet types used for SURF. Right: Detail of the Graffiti scene showing the size of the descriptor window at different scales.
    Fig. 2. Left: Detected interest points for a Sunflower field. This kind of scenes shows clearly the nature of the features from Hessian-based detectors. Middle: Haar wavelet types used for SURF. Right: Detail of the Graffiti scene showing the size of the descriptor window at different scales.
  • Fig. 3. The descriptor entries of a sub-region represent the nature of the underlying intensity pattern. Left: In case of a homogeneous region, all values are relatively low. Middle: In presence of frequencies in x direction, the value of ∑ |dx| is high, but all others remain low. If the intensity is gradually increasing in x direction, both values∑ dx and ∑ |dx| are high.
    Fig. 3. The descriptor entries of a sub-region represent the nature of the underlying intensity pattern. Left: In case of a homogeneous region, all values are relatively low. Middle: In presence of frequencies in x direction, the value of ∑ |dx| is high, but all others remain low. If the intensity is gradually increasing in x direction, both values∑ dx and ∑ |dx| are high.
  • Fig. 4. The recall vs. (1-precision) graph for different binning methods and two different matching strategies tested on the ’Graffiti’ sequence (image 1 and 3) with a view change of 30 degrees, compared to the current descriptors. The interest points are computed with our ’Fast Hessian’ detector. Note that the interest points are not affine invariant. The results are therefore not comparable to the ones in [8]. SURF-128 corresponds to the extended descriptor. Left: Similarity-threshold-based matching strategy. Right: Nearest-neighbour-ratio matching strategy (See section 5).
    Fig. 4. The recall vs. (1-precision) graph for different binning methods and two different matching strategies tested on the ’Graffiti’ sequence (image 1 and 3) with a view change of 30 degrees, compared to the current descriptors. The interest points are computed with our ’Fast Hessian’ detector. Note that the interest points are not affine invariant. The results are therefore not comparable to the ones in [8]. SURF-128 corresponds to the extended descriptor. Left: Similarity-threshold-based matching strategy. Right: Nearest-neighbour-ratio matching strategy (See section 5).
  • Table 1. Thresholds, number of detected points and calculation time for the detectors in our comparison. (First image of Graffiti scene, 800 × 640).
    Table 1. Thresholds, number of detected points and calculation time for the detectors in our comparison. (First image of Graffiti scene, 800 × 640).
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Topics: Scale-invariant feature transform (57%), GLOH (56%), Interest point detection (54%) ...read more

12,404 Citations


Open accessJournal ArticleDOI: 10.1016/J.CVIU.2007.09.014
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.

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  • Fig. 1. Using integral images, it takes only three additions and four memory accesses to calculate the sum of intensities inside a rectangular region of any size.
    Fig. 1. Using integral images, it takes only three additions and four memory accesses to calculate the sum of intensities inside a rectangular region of any size.
  • Fig. 2. Left to right: the (discretised and cropped) Gaussian second order partial derivative in y- (Lyy) and xy-direction (Lxy), respectively; our approximation for the second order Gaussian partial derivative in y- (Dyy) and xy-direction (Dxy). The grey regions are equal to zero.
    Fig. 2. Left to right: the (discretised and cropped) Gaussian second order partial derivative in y- (Lyy) and xy-direction (Lxy), respectively; our approximation for the second order Gaussian partial derivative in y- (Dyy) and xy-direction (Dxy). The grey regions are equal to zero.
  • Fig. 3. Top: Repeatability score for image rotation of up to 180 degrees. Hessian-based detectors have in general a lower repeatability score for angles around uneven multiples of π 4 . Bottom: Sample images from the Van Gogh sequence that was used. Fast-Hessian is the more accurate version of our detector (FH-15), as explained in section 3.3.
    Fig. 3. Top: Repeatability score for image rotation of up to 180 degrees. Hessian-based detectors have in general a lower repeatability score for angles around uneven multiples of π 4 . Bottom: Sample images from the Van Gogh sequence that was used. Fast-Hessian is the more accurate version of our detector (FH-15), as explained in section 3.3.
  • Fig. 4. Instead of iteratively reducing the image size (left), the use of integral images allows the up-scaling of the filter at constant cost (right).
    Fig. 4. Instead of iteratively reducing the image size (left), the use of integral images allows the up-scaling of the filter at constant cost (right).
  • Fig. 5. Filters Dyy (top) and Dxy (bottom) for two successive scale levels (9 × 9 and 15 × 15). The length of the dark lobe can only be increased by an even number of pixels in order to guarantee the presence of a central pixel (top).
    Fig. 5. Filters Dyy (top) and Dxy (bottom) for two successive scale levels (9 × 9 and 15 × 15). The length of the dark lobe can only be increased by an even number of pixels in order to guarantee the presence of a central pixel (top).
  • + 22

11,276 Citations


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

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Institution's top 5 most impactful journals

Social Science Research Network

1.6K papers, 26.7K citations

Physical Review B

1.2K papers, 38.8K citations

Angewandte Chemie

1.2K papers, 66.2K citations

Physical Review Letters

1K papers, 89.9K citations

bioRxiv

1K papers, 3.7K citations

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