Central Tendency and Variability

Weisberg, Herbert F.

doi:10.4135/9781412983617

Cited by 108 publications

(54 citation statements)

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“…While this conclusion is not challenged here, it is suggested that other summarizing statistics can be used as well. For instance, the trimean is robust to outliers, like the median, but still has attention to the extreme values in the distribution [36,37]. Using the trimean instead of the median reveals small variations in the ranking of color constancy algorithms, indicating that some color constancy algorithms (e.g., second-order Gray-Edge) have a wider distribution of illuminant estimate errors than others (e.g., first-order Gray-Edge).…”

Section: Discussionmentioning

confidence: 99%

“…Box plots are used to visualize the underlying distributions of the error metric of a given color constancy method, as an addition to a summarizing statistic. This summarizing statistic can be the median, as proposed by Hordley and Finlayson [7], or it can be the trimean, a statistic that is robust to outliers (the main advantage of the median over a statistic like the root mean square), but still has attention to the extreme values in the distribution [36,37]. The trimean (TM) can be calculated as the weighted average of the first, second, and third quantile Q 1 , Q 2 , and Q 3 , respectively: TM = 0.25Q 1 + 0.5Q 2 + 0.25Q 3 .…”

Section: A Distribution Of Errorsmentioning

confidence: 99%

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Perceptual analysis of distance measures for color constancy algorithms

2009

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Perceptual analysis of distance measures for color constancy algorithmsGijsenij, A.; Gevers, T.; Lucassen, M.P. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Color constancy algorithms are often evaluated by using a distance measure that is based on mathematical principles, such as the angular error. However, it is unknown whether these distance measures correlate to human vision. Therefore, the main goal of our paper is to analyze the correlation between several performance measures and the quality, obtained by using psychophysical experiments, of the output images generated by various color constancy algorithms. Subsequent issues that are addressed are the distribution of performance measures, suggesting additional and alternative information that can be provided to summarize the performance over a large set of images, and the perceptual significance of obtained improvements, i.e., the improvement that should be obtained before the difference becomes noticeable to a human observer.

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Section: Discussionmentioning

confidence: 99%

Section: A Distribution Of Errorsmentioning

confidence: 99%

Perceptual analysis of distance measures for color constancy algorithms

2009

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“…For univariate exploration of ordinal data, the usual measure of central tendency is the median [24]. However, given the limited range of values …”

Section: Statistical Methods For Muscle Site Datamentioning

confidence: 99%

The interpretation of skeletal muscle sites: a statistical approach

Robb

1998

Int. J. Osteoarchaeol.

174

168

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“…Furthermore, it has broad applications in statistics (see, for instance, Refs. [11,12]), engineering (see, for instance, Ref. [13,27]), mechanics (see, for instance, Ref.…”

Section: Introductionmentioning

confidence: 99%

Lagrangian Subspaces of Manifolds

Liu¹

2017

Lagrangian Mechanics

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In this chapter, we provide an overview on the Lagrangian subspaces of manifolds, including but not limited to, linear vector spaces, Riemannian manifolds, Finsler manifolds, and so on. There are also some new results developed in this chapter, such as finding the Lagrangians of complex spaces and providing new insights on the formula for measuring length, area, and volume in integral geometry. As an application, the symplectic structure determined by the Kähler form can be used to determine the symplectic form of the complex Holmes-Thompson volumes restricted on complex lines in integral geometry of complex Finsler space. Moreover, we show that the space of oriented lines and the tangent bundle of unit sphere in Minkowski space are symplectomorphic.

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Central Tendency and Variability

Cited by 108 publications

References 0 publications

Perceptual analysis of distance measures for color constancy algorithms

Perceptual analysis of distance measures for color constancy algorithms

The interpretation of skeletal muscle sites: a statistical approach

Lagrangian Subspaces of Manifolds

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