Nearest Neighbor Value Interpolation

This paper presents the nearest neighbor value (NNV) algorithm for high resolution (H.R.) image interpolation. The difference between the proposed algorithm and conventional nearest neighbor algorithm is that the concept applied, to estimate the missing pixel value, is guided by the nearest value rather than the distance. In other words, the proposed concept selects one pixel, among four direct...

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Nearest Neighbor Value Interpolation

This paper presents the nearest neighbor value (NNV) algorithm for high resolution (H.R.) image interpolation. The difference between the proposed algorithm and conventional nearest neighbor algorithm is that the concept applied, to estimate the missing pixel value, is guided by the nearest value rather than the distance. In other words, the proposed concept selects one pixel, among four direct...

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Presentation of K Nearest Neighbor Gaussian Interpolation and comparing it with Fuzzy Interpolation in Speech Recognition

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

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Presentation of K Nearest Neighbor Gaussian Interpolation and comparing it with Fuzzy Interpolation in Speech Recognition

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

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Investigating the Use of Nearest-neighbor Interpolation for Cancer Research

We investigate in how far interpolation mechanisms based on the nearest-neighbor rule (NNR) can support cancer research. The main objective is to use the NNR to predict the likelihood of tumorigenesis based on given risk factors. By using a genetic algorithm to optimize the parameters of the nearest-neighbor prediction, the performance of this interpolation method can be improved substantially....

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Nearest Neighbor Value Interpolation

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