Showing papers on "Resampling published in 1984"
19 Mar 1984
TL;DR: A digital resampling method is proposed which allows non-uniform and time-varying resamplings and is based on interpolated look-up in a large table of filter coefficients.
Abstract: A digital resampling method is proposed which allows non-uniform and time-varying resampling. The method is based on interpolated look-up in a large table of filter coefficients. One filter table handles all conversion factors. Formulas are given for determining the size of look-up table needed for a given precision requirement.
145 citations
TL;DR: In this paper, the problem of estimating an endpoint of a distribution is revisited, using the bootstrap and random subsample methods, and it is shown that one can in fact construct asymptotically valid confidence intervals in some situations.
Abstract: The problem of estimating an endpoint of a distribution is revisited, using the bootstrap and random subsample methods. Contrary to an example in Bickel and Freedman (1981) suggesting that these methods do not work here, it is shown that one can in fact construct asymptotically valid confidence intervals in some situations.
29 citations
TL;DR: In this article, a weighted resampling method analogous to the weighted jackknife developed by Hinkley (1977) is proposed for regression models, which is used for functions of a linear model.
23 citations
09 Jan 1984
TL;DR: Investigation of the image resampling requirements of remote sensing has indicated a need for improved convolution kernel design, and areas in which progress has been made include a recognition of the improved phase linearity of longer kernels and the need for similarity of the modulation transfer function (MTF) across all filters.
Abstract: Investigation of the image resampling requirements of remote sensing has indicated a need for improved resampling convolution kernel design. Areas in which progress has been made include a recognition of the improved phase linearity of longer kernels and the need for similarity of the modulation transfer function (MTF) across all filters. The computational capability required for the longer kernels is achieved with a dedicated signal processor.
4 citations
01 Jan 1984
TL;DR: It is shown that there is an optimum member of this 'parametric cubic convolution' family which minimizes the mean-squared radiometric error arising from interpolation and requires no additional computation time over the conventional cubic one.
Abstract: It is noted that the cubic resampling function is only one member of a family of functions, defined by the single parameter of the slope of the cubic function at its first zero crossing, whose other members are in some cases superior to the standard cubic. This superiority is especially noteworthy with respect to the extent of gray level overshoot induced by the resampling process at high contrast edges. It is shown that there is an optimum member of this 'parametric cubic convolution' family which minimizes the mean-squared radiometric error arising from interpolation. This interpolator requires no additional computation time over the conventional cubic one. These conclusions are supported and illustrated by resampling simulations with both a high resolution digitized aerial image and a Landsat Multispectral Scanner image.
4 citations
TL;DR: It is emphasized that nonstatistical inference remains a standard scientific approach, in spite of widespread use of many statistical tests, as an alternative to conventional statistical procedures for nonrandom sampling data.
Abstract: The implausibility of random sampling assumption in experimental teratology is pointed out. It is emphasized that nonstatistical inference remains a standard scientific approach, in spite of widespread use of many statistical tests. The randomization test can be introduced to experimental teratologjsts as an alternative to conventional statistical procedures for nonrandom sampling data. A program list for the randomization test written in BASIC for microcompter is given.
3 citations
2 citations
09 Jan 1984
TL;DR: A hardware architecture for real-time image resampling has been developed which will support a wide range of image Resampling tasks arising in remote sensing applications and which has the added advantage of providing automatic co-registration of images obtained in several spectral bands with separate sensor arrays.
Abstract: A hardware architecture for real-time image resampling has been developed which will support a wide range of image resampling tasks arising in remote sensing applications. In particular, local spatial sampling errors caused by misalignment of sensor arrays and optical defects, plus global spatial sampling errors caused by platform pointing errors, can be simultaneously rectified. This is achieved by referring all sample errors to the same fixed ideal sampling coordinate frame This has the added advantage of providing automatic co-registration of images obtained in several spectral bands with separate sensor arrays. The resulting architecture is modular and flexible due to a decomposition into independent parallel structures. The use of very large scale integrated circuits for memories and mul-tiplier/accumulators results in a design with a processing speed/power ratio in excess of 10° pixels per second per Watt and providing 1/16 pixel resampling accuracy.© (1984) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
2 citations