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Non-parametric self-calibration
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005In this paper, we develop a theory of non-parametric self-calibration. Recently, schemes have been devised for non-parametric laboratory calibration, but not for self-calibration. We allow an arbitrary warp to model the intrinsic mapping, with the only restriction that the camera is central and that the intrinsic mapping has a well-defined non-singular
David Nistér +2 more
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Non-Parametric Subject Prediction
2019Automatic subject prediction is a desirable feature for modern digital library systems, as manual indexing can no longer cope with the rapid growth of digital collections. This is an “extreme multi-label classification” problem, where the objective is to assign a small subset of the most relevant subjects from an extremely large label set.
Shenghui Wang 0001 +2 more
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Non‐parametric Regression for Circular Responses
Scandinavian Journal of Statistics, 2012Abstract.Regression with a circular response is a topic of current interest. We introduce non‐parametric smoothing for this problem. Simple adaptations of a weight function enable a unified formulation for both real‐line and circular predictors, whereas these cases have been tackled by quite distinct parametric methods. Additionally, we discuss various
MARCO DI MARZIO +2 more
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Non-parametric detection in underwater environments
International Conference on Acoustics, Speech, and Signal Processing, 1989Motivated by the recurring use of the generalized Gaussian family to model different underwater noise sources and the asymptotic performance levels of some commonly used detectors for this family, the authors examined the performance of these detectors for several different underwater noise sources.
Pamela A. Nielsen, John B. Thomas
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Non-parametric natural image matting
2009 16th IEEE International Conference on Image Processing (ICIP), 2009Natural image matting is an extremely challenging image processing problem due to its ill-posed nature. It often requires skilled user interaction to aid definition of foreground and background regions. Current algorithms use these predefined regions to build local foreground and background colour models. In this paper we propose a novel approach which
Sarim, Muhammad +4 more
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Non-parametric Statistical Methods
1987Basic statistics and econometrics courses stress methods based on assuming that the data or error term in regression models follow the normal distribution. Indeed, the efficiency of least squares estimates relies on the assumption of normality. In order to lessen the dependence of statistical inference on that assumption statisticians developed methods
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Non-parametric bootstrap recycling
Statistics and Computing, 2002The double bootstrap provides diagnostics for bootstrap calculations and, if need be, appropriate adjustments. The amount of computation involved is usually considerable, and recycling provides a less computer intensive alternative. Recycling consists of using repeatedly the same samples drawn from a recycling distribution G for estimation under each ...
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An introduction to non-parametric statistics
Analytical Methods, 2013Non-parametric statistical methods, which make fewer assumptions about population error distributions, have perhaps been unjustly neglected in the analytical sciences. A major advantage is that some of them are so simple that they can be used "at the bench."
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An unsupervised and non-parametric bayesian classifier
Pattern Recognition Letters, 2003Summary: We propose here an unsupervised Bayesian classifier based on a non-parametric expectation-maximization algorithm. The non-parametric aspect comes from the use of the orthogonal probability density function estimation, which is reduced to the estimation of the first Fourier coefficients of the pdf with respect to a given orthogonal basis.
Mourad Zribi, Faouzi Ghorbel
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To be parametric or non‐parametric, that is the question
BJOG: An International Journal of Obstetrics & Gynaecology, 2020Eric, Van Buren, Amy H, Herring
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