SIFT 와 SURF 알고리즘의 성능적 비교 분석

Comparative Analysis of the Performance of SIFT and SURF

  • 이용환 (단국대학교 응용컴퓨터공학과) ;
  • 박제호 (단국대학교 컴퓨터과학과) ;
  • 김영섭 (단국대학교 전자공학과)
  • Lee, Yong-Hwan (Department of Applied Computer Engineering, Dankook University) ;
  • Park, Je-Ho (Dept. of Computer Science, Dankook University) ;
  • Kim, Youngseop (Dept. of Electronic Engineering, Dankook University)
  • 투고 : 2013.09.03
  • 심사 : 2013.09.23
  • 발행 : 2013.09.30

초록

Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

키워드

과제정보

연구 과제 주관 기관 : 한국콘텐츠진흥원

참고문헌

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