한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제20권1호
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  • Pages.116-122
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  • 2010
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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표적의 기하학적 변환에 강인한 SIFT 기반의 표적 분류 알고리즘 설계

Design of a SIFT based Target Classification Algorithm robust to Geometric Transformation of Target

  • 투고 : 2009.06.06
  • 심사 : 2009.12.13
  • 발행 : 2010.02.25
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초록

본 논문은 표적의 회전, 크기 변화, 이동 변화, 자세변화 등의 기하학적 변환에 강인한 표적 분류 방법을 제안한다. 우선 표적의 회전, 크기변화, 이동 변화에 대해서는 SIFT(Scale-Invariant Feature Transform) 특징 벡터들의 유사도, 스케일비, 오리엔테이션의 범위들을 이용한 CM(Confidence Map)에 기반하여 표적을 분류한다. 한편 표적의 자세 변화에 대응하기 위해 다양한 각도에서 획득한 표적 영상의 DB(database)를 이용한다. 각도의 범위는 실행 시간과 샘플링 간격에 따른 성능을 비교, 분석하여 결정한다. 제안한 표적 분류 방법의 성능을 평가하기 위해 기하학적 변화가 있는 여러 가지 영상에 대해 실험한다. 실험을 통해 제안 알고리즘이 우수한 분류 성능을 보임을 증명한다.

This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

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참고문헌

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