Results 221 to 230 of about 93,046 (267)
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IEEE Transactions on Computers, 1982
Due to advances in VLSI technology, large scale arrays of microprocessors forming parallel processing systems have become feasible. The use of such a microprocessor array operating in the SIMD (single instruction stream-multiple data stream) mode to perform image resampling is explored.
Michael R. Warpenburg, Leah J. Siegel
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Due to advances in VLSI technology, large scale arrays of microprocessors forming parallel processing systems have become feasible. The use of such a microprocessor array operating in the SIMD (single instruction stream-multiple data stream) mode to perform image resampling is explored.
Michael R. Warpenburg, Leah J. Siegel
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Resampling on a Pseudohexagonal Grid
CVGIP: Graphical Models and Image Processing, 1994Abstract This paper investigates resampling techniques on a pseudohexagonal grid. Hexagonal grids are known to be advantageous in many respects for sampling and representing digital images in various computer vision and graphics applications. Currently, a real hexagonal grid device is still difficult to find.
Innchyn Her, Chi-Tseng Yuan
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Resampling for Spatial Scalability
2006 International Conference on Image Processing, 2006Resampling is a fundamental issue in the design of a spatially scalable video codec. The resampling procedure is responsible for down-sampling the high-resolution video sequence to generate lower resolution data, as well as upsampling the transmitted lower resolution data to predict the original high-resolution frames.
C. Andrew Segall, Aggelos K. Katsaggelos
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Resampling for Face Recognition
2003A number of applications require robust human face recognition under varying environmental lighting conditions and different facial expressions, which considerably vary the appearance of human face. However, in many face recognition applications, only a small number of training samples for each subject are available; these samples are not able to ...
Xiaoguang Lu, Anil K. Jain 0001
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On Importance Resampling for the Bootstrap
Biometrika, 1991SUMMARY We introduce an empirical method of importance resampling, which does not require analytical calculation of the resampling probabilities. Our method can easily be used as part of a general algorithm for Monte Carlo calculation of bootstrap confidence intervals and hypothesis tests.
KIM-ANH DO, PETER HALL
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Annealing by Increasing Resampling
2020Annealing by Increasing Resampling (AIR, for short) is a stochastic hill-climbing optimization algorithm that evaluates the objective function for resamplings with increasing size. At the beginning stages, AIR makes state transitions like a random walk, because it uses small resamplings for which evaluation has large error at high probability.
Naoya Higuchi +4 more
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The Deficiency Introduced by Resampling
Mathematical Methods of Statistics, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Antithetic Resampling for the Bootstrap
Biometrika, 1989Summary: An antithetic variates method for the bootstrap is proposed and discussed. It is applicable quite generally to bias estimation, distribution function estimation and quantile estimation, for example. It is based on an `antithetic permutation' of the sample, which amounts to ranking values of a certain function of the data.
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Resampling algorithms based on sample concatenation for imbalance learning
Knowledge-Based Systems, 2022Yuanxiang Dong, Hongbo Shi, suqin Ji
exaly