A perfect smoother.

By Paul H. C. Eilers. Analytical Chemistry, 2003

1,273 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/397851e47ac48d9838cb5643985df613d8f56051

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In Civil and Structural Engineering

In Computational Mechanics

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Citations in the corpus

Chromatogram baseline estimation and denoising using sparsity (BEADS) (2014, background): “Consequently, it appears beneficial to investigate generalized penalizations [13, 3, 33, 59], with less stringent models on either the signal, the background or the noise.”
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems (2009, neutral): “[152].”
An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data (2016, methodology): “feature is that the smoothing parameter (λ) can be automatic adjusted using cross-validation of good quality NDVI data points (Eilers, 2003).”
Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction (2015, methodology): “This can be efficiently done by performing the Smoother algorithm in [20] on the spectral locations of H previously estimated HR values, where H ≥ 10.”
Robust smoothing of gridded data in one and higher dimensions with missing values (2010, positive): “As a consequence, the search for a perfect universal smoother remains illusory (Eilers, 2003).”
A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, background): “The smoothness is also controlled by the order of differentiation which is fixed to 3 as proposed by Eilers (2003) for time series data sampled at regular intervals but with missing observations.”
Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum (2005, methodology): “The left and right endpoints of the interval are determined by the m/z values at the nearest local minima to the left and right, respectively, of the local maximum.”
Twenty years of P-splines (2015, background): “This leads to the Whittaker smoother (Whittaker, 1923; Eilers, 2003).”
Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology (2012, methodology): “Recently, the Whittaker smoother (Eilers, 2003) has also been adopted for smoothing remotely sensed timeseries (Atzberger & Eilers, 2011a,b).”
Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs (2013, neutral): “Further details are given in [99,145,146].”