한국정보과학회논문지:데이타베이스 (Journal of KIISE:Databases)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

시계열 이동평균 변환을 이용한 노이즈 제어 윤곽선 이미지 매칭

Noise Control Boundary Image Matching Using Time-Series Moving Average Transform

  • 김범수 (강원대학교 컴퓨터과학과) ;
  • 문양세 (강원대학교 컴퓨터학부 컴퓨터과학전공) ;
  • 김진호 (강원대학교 컴퓨터학부 컴퓨터과학전공)
  • 발행 : 2009.08.15
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 윤곽선 이미지 매칭에서 노이즈 제거 정도를 제어하기 위해 시계열 매칭의 이동평균 변환을 이용한다. 이동평균 변환을 윤곽선 이미지 매칭에 적용하게 된 동기는 이동평균 변환이 시계열의 노이즈를 감소시키므로, 이를 사용하면 윤곽선 이미지 매칭에서도 노이즈 제어 효과를 얻을 수 있을 것이라는 직관에 기반한다. 본 논문에서는 우선 윤곽선 이미지 매칭에 이동평균 변환을 적용한 $\kappa$-계수 이미지 매칭($\kappa$-order image matching)을 제안한다. 제안한 $\kappa$-계수 이미지 매칭은 윤곽선 이미지가 변환된 시계열에 $\kappa$-이동평균 변환을 적용하여 시계열(이미지) 간의 유사성을 판단한다. 다음으로, 대용량 이미지 데이터베이스를 대상으로 $\kappa$-계수 이미지 매칭을 수행하기 위한 인덱스 기반 매칭 방법을 제안하고, 그 정확성을 정형적으로 증명한다. 또한, 계수 $\kappa$와 매칭 결과와의 관계를 정형적으로 분석하고, 이에 기반하여 계수 $\kappa$를 변화시키면서 노이즈 제거 정도를 제어하는 방안을 제시한다. 실험 결과, $\kappa$-계수 이미지 매칭이 노이즈 제거 효과를 가짐을 확인하였으며, 제안한 인덱스 기반 매칭 방법은 순차 스캔에 비해 수 배 에서 수십 배 빠른 성능을 보이는 것으로 나타났다.

To achieve the noise reduction effect in boundary image matching, we use the moving average transform of time-series matching. Our motivation is based on an intuition that using the moving average transform we may exploit the noise reduction effect in boundary image matching as in time-series matching. To confirm this simple intuition, we first propose $\kappa$-order image matching, which applies the moving average transform to boundary image matching. A boundary image can be represented as a sequence in the time-series domain, and our $\kappa$-order image matching identifies similar images in this time-series domain by comparing the $\kappa$-moving average transformed sequences. Next, we propose an index-based matching method that efficiently performs $\kappa$-order image matching on a large volume of image databases, and formally prove the correctness of the index-based method. Moreover, we formally analyze the relationship between an order $\kappa$ and its matching result, and present a systematic way of controlling the noise reduction effect by changing the order $\kappa$. Experimental results show that our $\kappa$-order image matching exploits the noise reduction effect, and our index-based matching method outperforms the sequential scan by one or two orders of magnitude.

키워드

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