Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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New Kernel-Based Normality Recovery Method and Applications

새로운 커널 기반 정상 상태 복구 기법과 응용

  • 강대성 (고려대학교 제어계측공학과) ;
  • 박주영 (고려대학교 제어계측공학과)
  • Published : 2006.08.01
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Abstract

The SVDD(support vector data description) is one of the most important one-class support vector learning methods, which depends on the strategy of utilizing the balls defined on the feature space to discriminate the normal data from all other possible abnormal objects. This paper addresses on the extension of the SVDD method toward the problem of recovering the normal contents from the data contaminated with noises. The validity of the proposed de-noising method is shown via application to recovering the high-resolution images from the low-resolution images based on the high-resolution training data.

SVDD(support vector data description)는 가장 주요한 one-class 서포트 벡터 학습 방법론 중 하나로 비정상 물체에서 정상 데이터를 구분하기 위해서 특정 공간에서 정의된 구를 이용하는 전략을 쓰는 방법론이다. 본 논문에서는 SVDD를 이용해서 노이즈가 섞인 비정상 데이터를 노이즈가 제거된 정상 데이터로 복원하는 방법에 대해서 논한다. 그리고 고해상 도의 학습 데이터를 이용하여 저해상도로 주어진 시험 데이터 이미지를 고해상도의 이미지로 복원하는 문제에 적용함으로써 본 논문의 방법론이 어떻게 실용적으로 적용될 수 있는지에 대해서 다룬다.

Keywords

References

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