• Title/Summary/Keyword: Hierarchical discrete correlation

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A Fast MSRCR Algorithm Using Hierarchical Discrete Correlation (HDC를 이용한 고속 MSRCR 알고리즘)

  • Han, Kyu-Phil
    • Journal of Korea Multimedia Society
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    • v.13 no.11
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    • pp.1621-1629
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    • 2010
  • This paper presents an improved fast MSRCR algorithm that MSRs are commonly adopted at tone mapping in color vision. Conventional MSRs consist of three SSRs, which use three Gaussian functions with different scales as those surround ones. This convolution processes require much computation load. Therefore, the proposed algorithm adopts a hierarchical discrete correlation which is equivalent to Gaussian function and the Retinex process is only applied to the luminance channel in order to get a fast processing. A simple color preservation scheme is applied to the Retinex output from the luminance channel in the proposed MSRCR algorithm. Experimental results show that the proposed algorithm required less number of oprations and computation time about 1/9.5 and 1/3.5 times, respectively, than those of the simplest MSR and was equivalent to conventional MSRs.

Multi-dimension Categorical Data with Bayesian Network (베이지안 네트워크를 이용한 다차원 범주형 분석)

  • Kim, Yong-Chul
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.11 no.2
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    • pp.169-174
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    • 2018
  • In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.