2019
DOI: 10.1109/tsp.2019.2922154
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Domain-Informed Spline Interpolation

Abstract: Standard interpolation techniques are implicitly based on the assumption that the signal lies on a single homogeneous domain. In contrast, many naturally occurring signals lie on an inhomogeneous domain, such as brain activity associated to different brain tissue. We propose an interpolation method that instead exploits prior information about domain inhomogeneity, characterized by different, potentially overlapping, subdomains. As proof of concept, the focus is put on extending conventional shift-invariant B-… Show more

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Cited by 13 publications
(8 citation statements)
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“…One common approach is cortical surface smoothing, which has shown to provide increased sensitivity and specificity ( Coalson et al, 2018 ; Jo et al, 2007 ). Such methods have also been used to formulate smoothing approaches that respect tissue boundaries ( Behjat et al, 2019 ), preventing artifacts resulting from the mixing of signals from adjacent but differing tissue types during filtering. In both of these scenarios the anatomical information is provided by T1-weighted images.…”
Section: Introductionmentioning
confidence: 99%
“…One common approach is cortical surface smoothing, which has shown to provide increased sensitivity and specificity ( Coalson et al, 2018 ; Jo et al, 2007 ). Such methods have also been used to formulate smoothing approaches that respect tissue boundaries ( Behjat et al, 2019 ), preventing artifacts resulting from the mixing of signals from adjacent but differing tissue types during filtering. In both of these scenarios the anatomical information is provided by T1-weighted images.…”
Section: Introductionmentioning
confidence: 99%
“…One common approach is cortical surface smoothing, which has shown to provide increased sensitivity and specificity (Jo et al, 2007; Coalson et al, 2018). Such methods have also been used to formulate smoothing approaches that respect tissue boundaries (Behjat et al, 2019), preventing artifacts resulting from the mixing of signals from adjacent but differing tissue types during filtering. In both of these scenarios the anatomical information is provided by T1-weighted images.…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, the AIS sample A 2 is denoted as flag data when the constraints in equation 10are satisfied. We normalize the ship trajectory samples between A 1 and A 2 with the cubic spline interpolation, and, for more details, we suggest the reader to refer to [31]. e ship AIS data between A 2 and A 3 is normalized with the moving average model, and details can be found in [26].…”
Section: Ais Data Normalizationmentioning
confidence: 99%