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통계적 형상분석을 이용한 3차원 형상의 신뢰도 계수

A reliability measure of three dimensional shapes based on statistical shape analysis

  • 신상민 (동아대학교 경영정보학과)
  • Shin, Sang Min (Department of Management Information Systems, Dong-A University)
  • 투고 : 2018.07.23
  • 심사 : 2019.01.24
  • 발행 : 2019.02.28

초록

데이터에 대한 신뢰도는 측정결과의 전반적인 일관성을 의미한다. 이러한 신뢰도는 분석결과에 영향을 미치므로 측정된 데이터에 대한 신뢰도를 평가하는 방법은 지속적으로 연구되고 있으며, 일반적으로 크론바흐의 알파 계수(Cronbach's ${\alpha}$ coefficient)나 급내상관계수(intraclass correlation coefficient; ICC) 등이 신뢰도 평가에 주로 이용되고 있다. 그러나 특정 개체를 입체적으로 표현하기 위해 3차원 좌표로 구성되는 데이터에 대해서는 기존의 신뢰도 평가 척도를 적용할 수 없다. 따라서 본 연구에서는 통계적 형상분석을 이용하여 측정된 3차원 좌표로부터 개체들의 형상정보를 추출하고 그들의 형상변동을 분해하여, 3차원 형상에 대한 신뢰도를 평가할 수 있는 측도를 제안하고자 한다.

The reliability of the data means the overall consistency of the measurement results. Methods for evaluating reliability have been continuously studied because it affects the results of the statistical analysis. Cronbach's ${\alpha}$ coefficient or intraclass correlation coefficient is used for reliability evaluation; however, these measures cannot be applied to data consisting of three-dimensional coordinates to represent an object. In this study, we propose a measure to evaluate the reliability of three dimensional shapes based on statistical shape analysis that is made possible by extracting the shape information from the measured three dimensional coordinates and decomposing the shape variation.

키워드

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Figure 3.1. A three dimensional macaque skull (Dryden and Mardia, 2016, Chapter 1).

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Figure 3.2. A three dimensional mean shape and Procrustes fitted landmarks of macaque skulls.

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Figure 3.3. The means of RC with changes in r and λ.

Table 3.1. The variation of Procrustes sum of squares of 7 landmarks with changes in r and λ (mean ± sd)

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Table 3.2. The means and standard deviations of RC with changes in r and λ (mean ± sd)

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참고문헌

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