A chronology of interpolation: from ancient astronomy to modern signal and image processing

By Erik H. W. Meijering. Proceedings of the IEEE, 2002

726 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5361331422bf98f583b53682479d2f1a68f2153a

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In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Linear interpolation revitalized (2004, methodology): “We confirm our theoretical findings by performing several experiments: a cumulative rotation experiment and a zoom experiment.”
User‐friendly semiautomated assembly of accurate image mosaics in microscopy (2007, background): “As corroborated by the mathematical theory of approximation, this choice ensures that, in some precise sense, the amount of arbitrariness employed to fill the gaps between pixels is minimal for a given computational budget (Meijering, 2002; Thévenaz et al., 2000).”
A Review on Medical Image Registration as an Optimization Problem (2017, background): “Its mathematical structure relies on the function differential theory [33] or the approximation theory [34].”
Machine Learning for Fluid Mechanics (2019, background): “Large eddy simulations (LES) (Germano et al. 1991; Meneveau & Katz 2000) may also beneﬁt from super resolution to infer the high-resolution structure inside a low-resolution cell that is required to compute boundary conditions.”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, methodology): “Interpolation methods and their impact on medical image processing have been discussed in several tutorial articles by Meijering [170], Meijering et al.”
SEGCloud: Semantic Segmentation of 3D Point Clouds (2017, methodology): “ and still provide a ﬁne-grained labeling of 3D points using trilinear interpolation (TI) and CRF. We propose an end-to-end framework that leverages the advantages of 3D-FCNN, trilinear interpolation [47], and fully connected Conditional Random Fields(FC-CRF) [39, 37] to obtain ﬁne-grained 3D Segmen”
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting (2022, methodology): “Interpolation has been extensively used to augment the resolution of modeled signals in many fields such as signal and image processing (Meijering 2002).”
A Review of Geometric Transformations for Nonrigid Body Registration (2008, methodology): “In summary, the discrete function f∆x(j) with sample points j ∈ Z and sample interval ∆x can be interpolated to a continuous function f∆x(x) [67] as follows:”